High-Throughput Single-Cell Analysis Combining Proteomic and Genomic Information

ABSTRACT

Disclosed herein are methods for single-cell sequencing. In some examples, the methods include enriching a sample comprising a plurality of cells for cells of interest to produce an enriched cell sample; isolating one or more cells of interest in the enriched cell sample; and obtaining sequence information of one or more polynucleotides from each of the one or more isolated cells. Obtaining sequence information may include generating a molecularly indexed polynucleotide library from the one or more isolated cells. Enriching the sample may include focusing cells of interest in the sample using acoustic focusing.

RELATED APPLICATIONS

The present application claims priority under 35 U.S.C. § 119(e) to U.S. Provisional Application No. 62/118,412, filed on Feb. 19, 2015. The content of this related application is herein expressly incorporated by reference in its entirety.

BACKGROUND Field

The present disclosure relates generally to the field of molecular biology, and more particularly to high-throughput single-cell analysis.

Description of the Related Art

Methods and techniques for flow cytometry are useful in cell analysis, in particular deciphering expression profiles of cell surface proteins to determine the states of cells. Methods and techniques for mRNA sequencing are useful in cell analysis, in particular deciphering gene expression profiles to determine the states of cells using, for example, next generation sequencing (“NGS”). When performed on samples of both cells of interest and cells not of interest, these methods and techniques determine the average states of the cells in the samples. However, cell-to-cell variations can exist among different cells, thus these methods and techniques can have poor signal-to-noise ratios for cells of interest. Also, the gene expression profiles of cells may not be accurate proxies of the protein expression profiles of cells. Thus, there is a need for improved low cost methods of isolating and sequencing single cells, including cells of interest, as well as methods capable of efficiently and effectively combining genomic and proteomic information for cell analysis.

SUMMARY

The present disclosure provides methods for single-cell sequencing. In some embodiments, the methods include enriching a sample comprising a plurality of cells for cells of interest to produce an enriched cell sample; isolating one or more cells of interest in the enriched cell sample; and obtaining sequence information of one or more polynucleotides from each of the one or more isolated cells, wherein obtaining sequence information includes generating a molecularly indexed polynucleotide library from the one or more isolated cells. In some embodiments, the methods include enriching a sample comprising a plurality of cells for cells of interest to produce an enriched cell sample, wherein enriching the sample includes focusing cells of interest in the sample using acoustic focusing; isolating one or more cells of interest in the enriched cell sample; and obtaining sequence information of one or more polynucleotides from each of the one or more isolated cells.

In some embodiments, enriching the sample includes one or more of focusing cells of interest in the sample and depleting cells not of interest in the sample. Focusing the cells of interest can include focusing the cells of interest in the sample into a core stream of cells using, for example, acoustic focusing. Focusing the cells of interest in the sample can include focusing the cells of interest in the sample based on cell size. For example, focusing the cells of interest comprises focusing cells each having a size within a predetermined range. In some embodiments, depleting cells not of interest in the sample comprises depleting cells having a size that does not fall into the predetermined range. Acoustic focusing can include: suspending the plurality of cells in the sample in an elongated fluid-filled channel; and exposing said channel to an axial acoustic standing wave field parallel to the direction of flow, wherein said axial acoustic standing wave field drives the plurality of cells to positions of potential force minimum along the center axis of said channel to result in uniformly spaced cells. In some embodiments, focusing cells of interest includes hydrodynamic focusing, magnetic field focusing, electric field focusing, gravitational field focusing, optical field focusing, or a combination thereof. The method can, in some embodiments, include depleting one or more of interfering cells and debris in the sample.

In some embodiments, isolating the one or more cells of interest from the enriched cell sample includes sorting cells in the enriched cell sample with a flow cytometer. The flow cytometer can utilize fluorescence-activated cell sorting. Isolating the one or more cells of interest in the sample can include depositing the one or more cells of interest in the sample into one or more microtiter plates. Each of the one or more microtiter plates can have, for example, at least 96 wells.

In some embodiments, the one or more polynucleotides include RNA, such as mRNA, and can be obtained from exosome. The sequence information can, for example, include transcript counts of at least 10 genes, for example, CD4, FOX01, CD45RO, MYC, IL1R2, PRF1, GZMK, LGALS1, IL17F, IL23R, LYNX1, PRDM1, SELL, SMAD4, ICOS, IKZF5, RORC, AHRR, CTLA4, ITGB7, ENTPD1, CCR8, TSHR, TGFB2, IL12A, IL7R, HLA-DMA, CCR5, TIAF1, BCL6, BHLHE40, CXCR4, CD307c, CD3D, GSTP1, TCF7, CD3E, RNB6, RB1, MYB, CD3G, KRT8, CDH1, ERBB3, ERBB2, TCTN1, ESR1, CDKN1A, TFF3, ABCB1, ABCG2, ADAM23, AKT1, APC, AR, ATM, BAD, BCL2, BIRC5, BRCA1, BRCA2, C4A, CA12, CCNA1, CCND1, CCND2, CCNE1, CDH1, CDH13, CDK2, CD326, CDKN1A, CDKN1C, CDKN2A, CSF1, CST6, CTNNB1, CTSD, EGF, EGFR, EMAP-2, ERBB2, ERBB3, ESR1, ESR2, FOXA1, GATA3, GLI1, GPI, GRB7, GSTP1, HIC1, HPRT1, ID1, IGF1, IGF1R, IGFBP3, IL6, JUN, KRT18, KRT19, KRT5, KRT8, LAMP1, MAPK1, MAPK3, MAPK8, MGMT, MKI67, MLH1, MMP2, MMP9, MUC1, MYB, MYC, NME1, NOTCH1, NR3C1, PGR, PLAU, PRDM2, PSMB2, PSMB4, PTEN, PTGS2, PYCARD, RAB7A, RARA, RARB, RASSF1, RB1, REEP5, RNB6, SERPINE1, SFN, SFRP1, SLC39A6, SLIT2, SNAI2, SRC, TBC1D9. TCTN1, TFF3, TGFB1, THBS1, TP73, TWIST1, VEGFA, XBP1, CD3E, CD3G, CD3G, TCF7, ALCAM, CD25, ITGA6, THY1, PROM1, and CXCR4. In some embodiments, no more than 500 cells can be isolated from the enriched cell sample.

In some embodiments, obtaining sequence information includes sequencing the molecularly indexed polynucleotide library and possibly deconvoluting the sequencing result from sequencing the library. Deconvoluting the sequencing result, in some embodiments, includes using a software-as-a-service platform.

In some embodiments, the sample can include, or be, a biological sample, a clinical sample, an environmental sample, or a combination thereof. In some embodiments, the sample can include one or more of a biological fluid, tissue and cell from a patient. In some embodiments, the sample can include blood, urine, cerebrospinal fluid, pleural fluid, amniotic fluid, semen, saliva, bone marrow, a biopsy sample, or a combination thereof.

In some embodiments, the cells of interest in the sample include stem cells, cancer cells, blood cells, peripheral blood mononuclear cells, circulating tumor cells, breast cancer cells, cells at a cell cycle phase of desire, or a combination thereof. The methods can, in some embodiments, include one or more of determining genotype of the patient based on the obtained sequence information; determining phenotype of the patient based on the obtained sequence information; determining one or more genetic mutation of the patient based on the obtained sequence information; and predicting susceptibility of a patient to one or more diseases. At least one of the one or more diseases can be, for example, cancer or a hereditary disease. In some embodiments, less than 10%, 1%, 0.1%, or 0.01% of total number of cells in the sample are the cells of interest.

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1A-B are flowcharts showing non-limiting exemplary workflows of cell analysis, including, for example, cell enrichment and sorting, molecular indexing, library preparation, sequencing, and data analysis.

FIG. 2 is a non-limiting schematic illustration showing a non-limiting exemplary process for generating a molecularly indexed polynucleotide library from one or more cells.

FIG. 3 is a schematic illustration of a non-limiting embodiment of stochastic labeling and counting by hybridization to an array of support bound probes.

FIGS. 4A-B are non-limiting schematic illustrations of labeling target molecules and detection of labeled targets. FIG. 4A shows a schematic of labeling target molecules with a pool of labels. FIG. 4B shows a schematic of detection of labeled targets on an array having features that are label specific and target specific.

FIG. 5 is a non-limiting schematic illustration of acoustic focusing.

FIGS. 6A-C are non-limiting schematic illustrations of acoustic focusing.

FIG. 7 is a non-limiting schematic illustration of acoustic focusing.

FIG. 8 is a non-limiting schematic illustration of combining acoustic focusing and hydrodynamic focusing.

FIG. 9 is a non-limiting schematic illustration of a cell sorter system.

FIG. 10 is a flowchart showing non-limiting exemplary steps of data analysis.

FIGS. 11A-G show gating of single cells for next generation sequencing.

FIGS. 12A-B are bar graphs showing the number of reads and transcripts obtained from single cells.

FIG. 13 is a plot of counting reads vs. molecules showing that molecular indexing assay corrects for PCR bias.

FIGS. 14A-B are principal component analysis (“PCA”) plots of Tregs vs. non-Tregs and naïve vs. effector Tregs showing that surface marker phenotypes correlate with molecular profiles.

FIGS. 15A-C are PCA single-cell plots of CD45RA⁻CD15S⁺, CD161⁻ non-suppressive Tregs, and CD45RA⁺CD15S⁻ effector Tregs across time points.

FIGS. 16A-C are plots showing cell cycle analysis of single cells.

FIGS. 17A-B are plots showing accurate delivery of single cells and small numbers of cells.

FIGS. 18A-B are plots showing single cell isolation with a cell sorter.

FIGS. 19A-C show PCA clustering and gene expression profiles of Jurkat cells and T47D cells. FIG. 19A is a PCA plot showing clustering of Jurkat cells and T47D cells into two clusters. FIGS. 19B-C are bar charts showing gene expression profiles in cluster 1 of Jurkat cells and cluster 2 of T47D cells.

FIGS. 20A-B are plots showing relative protein expression of CD44 and Her2/Neu.

FIGS. 21A-D are plots showing three populations defined by Her2/Neu protein expressions in Jurkat, Hela, T47D, and SKBR3.

FIG. 22 is a table showing the breast cancer genes analyzed with molecular indexing

FIG. 23 is a heat map showing the analysis of the expressed target genes demonstrated a clear distinction between the four different cell types.

FIGS. 24A-B are PCA plots showing clustering by cell type with random Her2/Neu expressions supporting the heat map results.

FIGS. 25A-G are expression plots showing that step-wise increases in ERBB2, BRCA1, CDK2, MUC1, CDH1, CTSD, and CTSD.

DETAILED DESCRIPTION

In the following Detailed description, reference is made to the accompanying drawings, which form a part hereof. In the drawings, similar symbols typically identify similar components, unless context dictates otherwise. The illustrative embodiments described in the detailed description, drawings, and claims are not meant to be limiting. Other embodiments can be utilized, and other changes can be made, without departing from the spirit or scope of the subject matter presented herein. It will be readily understood that the aspects of the present disclosure, as generally described herein, and illustrated in the Figures, can be arranged, substituted, combined, separated, and designed in a wide variety of different configurations, all of which are explicitly contemplated and made part of the disclosure herein.

DEFINITIONS

Unless defined otherwise, technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the present disclosure belongs. See, e.g. Singleton et al., Dictionary of Microbiology and Molecular Biology 2nd ed., J. Wiley & Sons (New York, N.Y. 1994); Sambrook et al., Molecular Cloning, A Laboratory Manual, Cold Springs Harbor Press (Cold Springs Harbor, N.Y. 1989). For purposes of the present disclosure, the following terms are defined below.

As used herein the term “adaptor” can mean a sequence to facilitate amplification or sequencing of associated nucleic acids. The associated nucleic acids can comprise target nucleic acids. The associated nucleic acids can comprise one or more of sample labels and molecular labels. The adapters can be linear. The adaptors can be pre-adenylated adapters. The adaptors can be double- or single-stranded. One or more adaptor can be located on the 5′ or 3′ end of a nucleic acid. When the adaptors comprise known sequences on the 5′ and 3′ ends, the known sequences can be the same or different sequences. An adaptor located on the 5′ and/or 3′ ends of a polynucleotide can be capable of hybridizing to one or more oligonucleotides immobilized on a surface. An adapter can comprise a universal sequence. A universal sequence can be a region of nucleotide sequence that is common to two or more nucleic acid molecules. The two or more nucleic acid molecules can also have regions of sequence differences. Thus, for example, the 5′ adapters can comprise identical or universal nucleic acid sequences and the 3′ adapters can comprise identical or universal sequences. A universal sequence that may be present in different members of a plurality of nucleic acid molecules can allow the replication or amplification of multiple different sequences using a single universal primer that is complementary to the universal sequence. Similarly, at least one, two (e.g., a pair) or more universal sequences that may be present in different members of a collection of nucleic acid molecules can allow the replication or amplification of multiple different sequences using at least one, two (e.g., a pair) or more single universal primers that are complementary to the universal sequences. Thus, a universal primer includes a sequence that can hybridize to such a universal sequence. The target nucleic acid sequence-bearing molecules may be modified to attach universal adapters (e.g., non-target nucleic acid sequences) to one or both ends of the different target nucleic acid sequences, the adapters providing sites for hybridization of universal primers. The adaptors at the 5′ and 3′ ends of the adapter-target-adapters can be the same or different.

As used herein, the term “associated” or “associated with” can mean that two or more species are identifiable as being co-located at a point in time. An association can mean that two or more species are or were within a similar container. An association can be an informatics association, where for example digital information regarding two or more species is stored and can be used to determine that one or more of the species were co-located at a point in time. An association can also be a physical association. In some embodiments two or more associated species are “tethered”, “attached”, or “immobilized” to one another or to a common solid or semisolid surface. An association may refer to covalent or non-covalent means for attaching labels to solid or semi-solid supports such as beads. An association may be a covalent bond between a target and a label.

As used herein, the term “complementary” can refer to the capacity for precise pairing between two nucleotides. For example, if a nucleotide at a given position of a nucleic acid is capable of hydrogen bonding with a nucleotide of another nucleic acid, then the two nucleic acids are considered to be complementary to one another at that position. Complementarity between two single-stranded nucleic acid molecules may be “partial,” in which only some of the nucleotides bind, or it may be complete when total complementarity exists between the single-stranded molecules. A first nucleotide sequence can be said to be the “complement” of a second sequence if the first nucleotide sequence is complementary to the second nucleotide sequence. A first nucleotide sequence can be said to be the “reverse complement” of a second sequence, if the first nucleotide sequence is complementary to a sequence that is the reverse (i.e., the order of the nucleotides is reversed) of the second sequence. As used herein, the terms “complement”, “complementary”, and “reverse complement” can be used interchangeably. It is understood from the disclosure that if a molecule can hybridize to another molecule it may be the complement of the molecule that is hybridizing.

As used herein, the term “digital counting” can refer to a method for estimating a number of target molecules in a sample. Digital counting can include the step of determining a number of unique labels that have been associated with targets in a sample. This stochastic methodology transforms the problem of counting molecules from one of locating and identifying identical molecules to a series of yes/no digital questions regarding detection of a set of predefined labels.

As used herein, the term “label” or “labels” can refer to nucleic acid codes associated with a target within a sample. A label can be, for example, a nucleic acid label. A label can be an entirely or partially amplifiable label. A label can be entirely or partially sequencable label. A label can be a portion of a native nucleic acid that is identifiable as distinct. A label can be a known sequence. A label can comprise a junction of nucleic acid sequences, for example a junction of a native and non-native sequence. As used herein, the term “label” can be used interchangeably with the terms, “index”, “tag,” or “label-tag.” Labels can convey information. For example, in various embodiments, labels can be used to determine an identity of a sample, a source of a sample, an identity of a cell, and/or a target.

As used herein, the term “mRNA” or sometimes refer by “mRNA transcripts” include, but is not limited to pre-mRNA transcript(s), transcript processing intermediates, mature mRNA(s) ready for translation and transcripts of the gene or genes, or nucleic acids derived from the mRNA transcript(s). Transcript processing can include splicing, editing and degradation. As used herein, a nucleic acid derived from an mRNA transcript refers to a nucleic acid for whose synthesis the mRNA transcript or a subsequence thereof has ultimately served as a template. Thus, a cDNA reverse transcribed from an mRNA, an RNA transcribed from that cDNA, a DNA amplified from the cDNA, an RNA transcribed from the amplified DNA, etc., are all derived from the mRNA transcript and detection of such derived products is indicative of the presence and/or abundance of the original transcript in a sample. Thus, mRNA derived samples include, but are not limited to, mRNA transcripts of the gene or genes, cDNA reverse transcribed from the mRNA, cRNA transcribed from the cDNA, DNA amplified from the genes, RNA transcribed from amplified DNA, and the like.

As used herein, the term “next generation sequencing” refers to sequencing technologies having increased throughput as compared to the traditional Sanger- and capillary electrophoresis-based approaches, for example with the ability to generate hundreds of thousands or millions of relatively short sequence reads at a time. Examples of next generation sequencing techniques include, but are not limited to, sequencing by synthesis, sequencing by ligation, and sequencing by hybridization. Examples of next generations sequencing methods include, but are not limited to, pyrosequencing as used by the GS Junior and GS FLX Systems (454 Life Sciences, Bradford, Conn.); sequencing by synthesis as used by Miseq and Solexa system (Illumina, Inc., San Diego, Calif.); the SOLiD™ (Sequencing by Oligonucleotide Ligation and Detection) system and Ion Torrent Sequencing systems such as the Personal Genome Machine or the Proton Sequencer (Thermo Fisher Scientific, Waltham, Mass.), and nanopore sequencing systems (Oxford Nanopore Technologies, Oxford, united Kingdom).

As used herein, the term “non-depleting reservoirs” can refer to a pool of stochastic barcodes made up of many different labels. A non-depleting reservoir can comprise large numbers of different stochastic barcodes such that when the non-depleting reservoir is associated with a pool of targets each target is likely to be associated with a unique stochastic barcode. The uniqueness of each labeled target molecule can be determined by the statistics of random choice, and depends on the number of copies of identical target molecules in the collection compared to the diversity of labels. The size of the resulting set of labeled target molecules can be determined by the stochastic nature of the barcoding process, and analysis of the number of stochastic barcodes detected then allows calculation of the number of target molecules present in the original collection or sample. When the ratio of the number of copies of a target molecule present to the number of unique stochastic barcodes is low, the labeled target molecules are highly unique (i.e. there is a very low probability that more than one target molecule will have been labeled with a given label).

As used herein, a “nucleic acid” can generally refer to a polynucleotide sequence, or fragment thereof. A nucleic acid can comprise nucleotides. A nucleic acid can be exogenous or endogenous to a cell. A nucleic acid can exist in a cell-free environment. A nucleic acid can be a gene or fragment thereof. A nucleic acid can be DNA. A nucleic acid can be RNA. A nucleic acid can comprise one or more analogs (e.g. altered backbone, sugar, or nucleobase). Some non-limiting examples of analogs include: 5-bromouracil, peptide nucleic acid, xeno nucleic acid, morpholinos, locked nucleic acids, glycol nucleic acids, threose nucleic acids, dideoxynucleotides, cordycepin, 7-deaza-GTP, fluorophores (e.g. rhodamine or fluorescein linked to the sugar), thiol containing nucleotides, biotin linked nucleotides, fluorescent base analogs, CpG islands, methyl-7-guanosine, methylated nucleotides, inosine, thiouridine, pseudouridine, dihydrouridine, queuosine, and wyosine. “Nucleic acid”, “polynucleotide, “target polynucleotide”, and “target nucleic acid” can be used interchangeably.

A nucleic acid can comprise one or more modifications (e.g., a base modification, a backbone modification), to provide the nucleic acid with a new or enhanced feature (e.g., improved stability). A nucleic acid can comprise a nucleic acid affinity tag. A nucleoside can be a base-sugar combination. The base portion of the nucleoside can be a heterocyclic base. The two most common classes of such heterocyclic bases are the purines and the pyrimidines. Nucleotides can be nucleosides that further include a phosphate group covalently linked to the sugar portion of the nucleoside. For those nucleosides that include a pentofuranosyl sugar, the phosphate group can be linked to the 2′, the 3′, or the 5′ hydroxyl moiety of the sugar. In forming nucleic acids, the phosphate groups can covalently link adjacent nucleosides to one another to form a linear polymeric compound. In turn, the respective ends of this linear polymeric compound can be further joined to form a circular compound; however, linear compounds are generally suitable. In addition, linear compounds may have internal nucleotide base complementarity and may therefore fold in a manner as to produce a fully or partially double-stranded compound. Within nucleic acids, the phosphate groups can commonly be referred to as forming the internucleoside backbone of the nucleic acid. The linkage or backbone can be a 3′ to 5′ phosphodiester linkage.

A nucleic acid can comprise a modified backbone and/or modified internucleoside linkages. Modified backbones can include those that retain a phosphorus atom in the backbone and those that do not have a phosphorus atom in the backbone. Suitable modified nucleic acid backbones containing a phosphorus atom therein can include, for example, phosphorothioates, chiral phosphorothioates, phosphorodithioates, phosphotriesters, aminoalkyl phosphotriesters, methyl and other alkyl phosphonate such as 3′-alkylene phosphonates, 5′-alkylene phosphonates, chiral phosphonates, phosphinates, phosphoramidates including 3′-amino phosphoramidate and aminoalkyl phosphoramidates, phosphorodiamidates, thionophosphoramidates, thionoalkylphosphonates, thionoalkylphosphotriesters, selenophosphates, and boranophosphates having normal 3′-5′ linkages, 2′-5′ linked analogs, and those having inverted polarity wherein one or more internucleotide linkages is a 3′ to 3′, a 5′ to 5′ or a 2′ to 2′ linkage.

A nucleic acid can comprise polynucleotide backbones that are formed by short chain alkyl or cycloalkyl internucleoside linkages, mixed heteroatom and alkyl or cycloalkyl internucleoside linkages, or one or more short chain heteroatomic or heterocyclic internucleoside linkages. These can include those having morpholino linkages (formed in part from the sugar portion of a nucleoside); siloxane backbones; sulfide, sulfoxide and sulfone backbones; formacetyl and thioformacetyl backbones; methylene formacetyl and thioformacetyl backbones; riboacetyl backbones; alkene containing backbones; sulfamate backbones; methyleneimino and methylenehydrazino backbones; sulfonate and sulfonamide backbones; amide backbones; and others having mixed N, O, S and CH2 component parts.

A nucleic acid can comprise a nucleic acid mimetic. The term “mimetic” can be intended to include polynucleotides wherein only the furanose ring or both the furanose ring and the internucleotide linkage are replaced with non-furanose groups, replacement of only the furanose ring can also be referred as being a sugar surrogate. The heterocyclic base moiety or a modified heterocyclic base moiety can be maintained for hybridization with an appropriate target nucleic acid. One such nucleic acid can be a peptide nucleic acid (PNA). In a PNA, the sugar-backbone of a polynucleotide can be replaced with an amide containing backbone, in particular an aminoethylglycine backbone. The nucleotides can be retained and are bound directly or indirectly to aza nitrogen atoms of the amide portion of the backbone. The backbone in PNA compounds can comprise two or more linked aminoethylglycine units which gives PNA an amide containing backbone. The heterocyclic base moieties can be bound directly or indirectly to aza nitrogen atoms of the amide portion of the backbone.

A nucleic acid can comprise a morpholino backbone structure. For example, a nucleic acid can comprise a 6-membered morpholino ring in place of a ribose ring. In some of these embodiments, a phosphorodiamidate or other non-phosphodiester internucleoside linkage can replace a phosphodiester linkage.

A nucleic acid can comprise linked morpholino units (i.e. morpholino nucleic acid) having heterocyclic bases attached to the morpholino ring. Linking groups can link the morpholino monomeric units in a morpholino nucleic acid. Non-ionic morpholino-based oligomeric compounds can have less undesired interactions with cellular proteins. Morpholino-based polynucleotides can be nonionic mimics of nucleic acids. A variety of compounds within the morpholino class can be joined using different linking groups. A further class of polynucleotide mimetic can be referred to as cyclohexenyl nucleic acids (CeNA). The furanose ring normally present in a nucleic acid molecule can be replaced with a cyclohexenyl ring. CeNA DMT protected phosphoramidite monomers can be prepared and used for oligomeric compound synthesis using phosphoramidite chemistry. The incorporation of CeNA monomers into a nucleic acid chain can increase the stability of a DNA/RNA hybrid. CeNA oligoadenylates can form complexes with nucleic acid complements with similar stability to the native complexes. A further modification can include Locked Nucleic Acids (LNAs) in which the 2′-hydroxyl group is linked to the 4′ carbon atom of the sugar ring thereby forming a 2′-C, 4′-C-oxymethylene linkage thereby forming a bicyclic sugar moiety. The linkage can be a methylene (—CH2—), group bridging the 2′ oxygen atom and the 4′ carbon atom wherein n is 1 or 2. LNA and LNA analogs can display very high duplex thermal stabilities with complementary nucleic acid (Tm=+3 to +10° C.), stability towards 3′-exonucleolytic degradation and good solubility properties.

A nucleic acid may also include nucleobase (often referred to simply as “base”) modifications or substitutions. As used herein, “unmodified” or “natural” nucleobases can include the purine bases, (e.g. adenine (A) and guanine (G)), and the pyrimidine bases, (e.g. thymine (T), cytosine (C) and uracil (U)). Modified nucleobases can include other synthetic and natural nucleobases such as 5-methylcytosine (5-me-C), 5-hydroxymethyl cytosine, xanthine, hypoxanthine, 2-aminoadenine, 6-methyl and other alkyl derivatives of adenine and guanine, 2-propyl and other alkyl derivatives of adenine and guanine, 2-thiouracil, 2-thiothymine and 2-thiocytosine, 5-halouracil and cytosine, 5-propynyl (—C═C—CH3) uracil and cytosine and other alkynyl derivatives of pyrimidine bases, 6-azo uracil, cytosine and thymine, 5-uracil (pseudouracil), 4-thiouracil, 8-halo, 8-amino, 8-thiol, 8-thioalkyl, 8-hydroxyl and other 8-substituted adenines and guanines, 5-halo particularly 5-bromo, 5-trifluoromethyl and other 5-substituted uracils and cytosines, 7-methylguanine and 7-methyladenine, 2-F-adenine, 2-aminoadenine, 8-azaguanine and 8-azaadenine, 7-deazaguanine and 7-deazaadenine and 3-deazaguanine and 3-deazaadenine. Modified nucleobases can include tricyclic pyrimidines such as phenoxazine cytidine(1H-pyrimido(5,4-b)(1,4)benzoxazin-2(3H)-one), phenothiazine cytidine (1H-pyrimido(5,4-b)(1,4)benzothiazin-2(3H)-one), G-clamps such as a substituted phenoxazine cytidine (e.g. 9-(2-aminoethoxy)-H-pyrimido(5,4-(b) (1,4)benzoxazin-2(3H)-one), phenothiazine cytidine (1H-pyrimido(5,4-b)(1,4)benzothiazin-2(3H)-one), G-clamps such as a substituted phenoxazine cytidine (e.g. 9-(2-aminoethoxy)-H-pyrimido(5,4-(b) (1,4)benzoxazin-2(3H)-one), carbazole cytidine (2H-pyrimido(4,5-b)indol-2-one), pyridoindole cytidine (H-pyrido(3′,':4,5)pyrrolo[2,3-d]pyrimidin-2-one).

As used herein, the term “solid support” can refer to discrete solid or semi-solid surfaces to which a plurality of molecular labels or stochastic barcodes may be attached. A solid support may encompass any type of solid, porous, or hollow sphere, ball, bearing, cylinder, or other similar configuration composed of plastic, ceramic, metal, or polymeric material (e.g., hydrogel) onto which a nucleic acid may be immobilized (e.g., covalently or non-covalently). A solid support may comprise a discrete particle that may be spherical (e.g., microspheres) or have a non-spherical or irregular shape, such as cubic, cuboid, pyramidal, cylindrical, conical, oblong, or disc-shaped, and the like. A solid support may be used interchangeably with the term “bead.”

As used herein, the term “stochastic barcode” can refer to a polynucleotide sequence comprising labels. A stochastic barcode can be a polynucleotide sequence that can be used for stochastic labeling. Stochastic barcodes can be used to quantify targets within a sample. A stochastic barcode associated with a target can be called a stochastic barcode-target or stochastic barcode-tag-target.

As used herein, the term “stochastic barcoding” can refer to the random labeling (e.g., barcoding) of nucleic acids. Stochastic barcoding can utilize a recursive Poisson strategy to associate and quantify labels associated with targets.

As used herein, the term “target” can refer to a composition which can be associated with a stochastic barcode or a molecular identifier label. Exemplary suitable targets for analysis by the disclosed methods, devices, and systems include oligonucleotides, DNA, RNA, mRNA, microRNA, tRNA, and the like. Targets can be single or double stranded. In some embodiments targets can be proteins. In some embodiments targets are lipids.

The term “reverse transcriptase” can refer to a group of enzymes having reverse transcriptase activity (i.e., that catalyze synthesis of DNA from an RNA template). In general, such enzymes include, but are not limited to, retroviral reverse transcriptase, retrotransposon reverse transcriptase, retroplasmid reverse transcriptases, retron reverse transcriptases, bacterial reverse transcriptases, group II intron-derived reverse transcriptase, and mutants, variants or derivatives thereof. Non-retroviral reverse transcriptases include non-LTR retrotransposon reverse transcriptases, retroplasmid reverse transcriptases, retron reverse transciptases, and group II intron reverse transcriptases. Examples of group II intron reverse transcriptases include the Lactococc s lactis Ll.LtrB intron reverse transcriptase, the Thermosynechococcus elongates TeI4c intron reverse transcriptase, or the Geobacillus stearothermophilus GsI-IIC intron reverse transcriptase. Other classes of reverse transcriptases can include many classes of non-retroviral reverse transcriptases (i.e., retrons, group II introns, and diversity-generating retroelements among others).

In the present disclosure, methods for cost-effective, high throughput, cell analysis, such as single-cell analysis, are disclosed. The methods are useful for high-resolution genomic studies, including NGS, qPCR, and microarray analysis. In some embodiments, the methods disclosed herein combine cell sorting and molecular indexing. The methods can also, in some embodiments, include an enrichment step before cell sorting and molecular indexing are performed. In some embodiments, the methods include the use of flow cytometry such as fluorescence-activated cell sorting (“FACS”) to isolate cells of interest. Flow cytometers capable of isolating single cells include, but are not limited to BD (Franklin Lakes, N.J.) FACSJazz™ cell sorter and BD FACSSeg™ cell sorter. Molecular indexing techniques include, for example, the Precise™ Molecular Indexing™ technology from Cellular Research, Inc. (Palo Alto, Calif.). The combination of proteomic and genomic analyses as disclosed herein allows comprehensive investigation of cells. For example, molecular indexing techniques can be used to investigate cell activities at transcription level, e.g., quantifying mRNA transcripts in a cell of interest, and cell sorting methods can be used to investigate cell activities at translation level, e.g., determining protein expression by the cell. The knowledge of cell activities at multiple levels can provide different aspects of the cell biology and detect abnormalities in the cell, e.g., a lack of correlation between transcription and translation.

Some embodiments disclosed herein provide methods of polynucleotide sequencing, such as single-cell polynucleotide sequencing. The methods include, in some embodiments, enriching a sample comprising a plurality of cells for cells of interest to produce an enriched cell sample; isolating one or more cells of interest from the enriched cell sample; and obtaining sequence information of one or more polynucleotides from each of the one or more isolated cells. In some embodiments, the methods include: enriching a sample comprising a plurality of cells for cells of interest to produce an enriched cell sample; isolating one or more cells of interest from the enriched cell sample; and obtaining sequence information of one or more polynucleotides from each of the one or more isolated cells, wherein obtaining sequence information includes generating a molecularly indexed polynucleotide library from the one or more isolated cells. In some embodiments, enriching the sample can include, for example, focusing the cells of interest in the sample. Cells of interest can be focused into a core stream of cells using, for example, acoustic focusing.

FIG. 1A is a flowchart showing a non-limiting exemplary process 100A combining, the steps of cell enrichment, cell sorting, and obtaining sequence information for the analysis of a sample. The method begins at 104A. At 110A, the method can enrich cells of interest in the sample by, for example, magnetically removing cells not of interest, interfering cells, and debris and acoustically focusing cells of interest in the sample. At 116A, cells of interest are isolated from the enriched cell sample. The isolation can be carried out by, for example, cell sorting including single-cell sorting. The sequence information of the isolated cells can be obtained at 120A by, for example, molecular indexing and sequencing such as NGS. Sequence information from step 120A can optionally be analyzed at 124A before the method completes at 128A. In the method, one or more of the enrichment step 110A and cell sorting step 116A can utilize focusing techniques. Non-limiting examples of focusing techniques include, but are not limited to acoustic focusing, hydrodynamic focusing, magnetic field focusing, electric field focusing, gravitational field focusing, optical field focusing, and any combinations thereof. In some embodiments, the enrichment step 110A can utilize techniques that magnetically deplete interfering cells and debris.

FIG. 1B is a flowchart showing a non-limiting exemplary process 100B from cell enrichment and sorting, molecular indexing, library preparation, sequencing, and data analysis. The process begins at 104B. One or more inline enrichment systems can, for example, magnetically remove cells not of interest, interfering cells, and debris from a sample at 108B. An inline enrichment system can utilize the BD Imag™ magnetic separation beads that bind cells not of interest. Cells can be acoustically focused at 112B to increase the concentration of cells of interest prior to cell sorting at 116B. Cells of interest in the sample can be stained for sorting at 116B using a cell sorter. Examples of cell sorters include, but are not limited to, BD FACSJazz™ cell sorter, a BD FACSseq™ cell sorter, a Bio-Rad Laboratories, Inc. (Hercules, Calif.) S3e™ Cell Sorter, a Sony Biotechnology Inc. (San Jose, Calif.) SH800 cell sorter, a Beckman Coulter Inc. (Brea, Calif.) MoFlo™ XDP cell sorter. After sorting cells at 116B, cells can be lysed for molecular indexing and library preparation at 120B and sequencing at 122B. Sequence information from step 122B can be optionally analyzed at 124B before the method completes at 128B.

In some embodiments, it can be advantageous to have an enrichment step in the method disclosed herein because the step can allow cell sorting to proceed more rapidly with less interference by cells not of interest, interfering cells, and debris. In some embodiments, utilizing acoustic focusing prior to cell sorting enables effective and fast enrichment of cells of interest. This can provide for a reduction of sorting time, for example from hours to minutes and seconds. As would be appreciated by persons of skill in the art, rapid sorting methods can prevent deterioration of cell health, changes in physiological conditions and states of cells, and/or cell death due to long processing time. In some embodiments, populations of live cells can be analyzed at the same point in their life cycles or in their normal physiological conditions and states.

Molecular Indexing and Library Generation

The single-cell sequencing methods disclosed herein can include obtaining sequence information by molecularly indexing the targets from one or more of the isolated cells from the sample. The targets can, for example, be polynucleotides. The polynucleotides can be, for example, DNA or RNA (e.g., mRNA). Molecular indexing (sometimes referred to as molecular barcoding or molecular tagging) can be used, for example, for high-sensitivity single molecular counting. For example, a collection of identical polynucleotide molecules from one or more of the isolated cells can be attached to a diverse set of labels for molecular indexing. Each of the labels can comprise, for example, a molecular label (also known as molecular index). In some embodiments, the method comprises molecularly indexing the polynucleotides from 1, 5, 10, 15, 20, 25, 50, 75, 100, 200, 300, 400, 500, 600, 800, 1000, 2000, 5000, 10000 cells, or a number or a range between any two of these values.

Molecular indexing can, for example, be used to identify the origin of an indexed polynucleotide (e.g., indicating from which tissue, cell and/or container the indexed polynucleotide is from) and/or to inform the identity of the indexed polynucleotide. The container can be a plate, a well, a droplet, a partition, a tube, or like. The indexed polynucleotide can comprise, for example, the polynucleotide to be indexed (e.g., an mRNA, a genomic DNA, or a cDNA) and a label region comprising one or more labels. In some embodiments, the indexed polynucleotide can further comprise one or more of a universal PCR region and an adaptor region. As an example, the indexed polynucleotide can be situated in a container (e.g., a microtiter plate), and the indexed polynucleotide can further include a unique label (e.g., a sample barcode) for identifying the plate in which the index polynucleotide is situated. An example of the region for identifying the plate is a plate index. The label region can, in some embodiments, comprise two or more labels. For example, the label region can include a molecular label (also known as a molecular index) and a sample label (also known as a sample barcode). The length of the labels can vary. For example, the label (e.g., the molecular label or the sample label) can be, or be about, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 15, 20 nucleotides in length, or longer. In some embodiments, the molecular label is, or is about, 5 nucleotides in length, and the sample label is, or is about, 5 nucleotides in length. In some embodiments, the molecular label is, or is about, 10 nucleotides in length, and the sample label is, or is about, 10 nucleotides in length.

In some embodiments, molecularly indexing the polynucleotides comprises generating a molecularly indexed polynucleotide library from one or more of the isolated cells. Generating a molecularly indexed polynucleotide library includes generating a plurality of indexed polynucleotides from the one or more of the isolated cells. For example, for a molecularly indexed polynucleotide library comprising a first indexed polynucleotide and a second indexed polynucleotide, the label region of the first indexed polynucleotide can differ from the label region of the second indexed polynucleotide by at least one, two, three, four, or five nucleotides. In some embodiments, generating a molecularly indexed polynucleotide library includes contacting a plurality of mRNA molecules with a plurality of oligonucleotides including a poly(T) region and a label region; and conducting a first strand synthesis using a reverse transcriptase to produce single-strand labeled cDNA molecules each comprising a cDNA region and a label region, wherein the plurality of mRNA molecules includes at least two mRNA molecules of different sequences and the plurality of oligonucleotides includes at least two oligonucleotides of different sequences. Generating a molecularly indexed polynucleotide library can further comprise amplifying the single-strand labeled cDNA molecules to produce double-strand labeled cDNA molecules; and conducting nested PCR on the double-strand labeled cDNA molecules to produce labeled amplicons. In some embodiments, the method can include generating an adaptor-labeled amplicon.

Molecular indexing uses nucleic acid barcodes or tags to label individual DNA or RNA molecules. In some embodiments, it involves adding DNA barcodes or tags to cDNA molecules as they are generated from mRNA. Nested PCR can be performed to minimize PCR amplification bias. Adaptors can be added for sequencing using, for example, NGS.

FIG. 2 is a schematic illustration showing a non-limiting exemplary process of generating a molecularly indexed polynucleotide library from one or more cells. As shown in step 1, the reverse transcription process can encode each mRNA molecule with a unique molecular label, a sample label, and a universal PCR site. Molecular labels are also known as molecular barcodes and molecular indices. Sample labels are also known as sample barcodes and sample indices. The labels enable all samples in one or more multitier plates in, for example, 96-well format to be pooled together for subsequent steps. In particular, RNA molecules 202 can be reverse transcribed to produce labeled cDNA molecules 204, including a cDNA region 206, by the stochastic hybridization of a set of molecular identifier labels 210 to the poly(A) tail region 208 of the RNA molecules 202. Each of the molecular identifier labels 210 can comprise a poly(dT) region 212, a label region 214, and a universal PCR region 216. In some embodiments, the label region 214 can comprise a molecular label 218 and a sample label 220. The molecular label 218 can be 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, or a number or a range between any of these values, of nucleotides in length. The sample label 220 can be 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, or a number or a range between any of these values, of nucleotides in length. In some embodiments, the label region 214 can comprise 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, or a number or a range between any of these values, different labels, such as a molecular label 218 and a sample label 220. Each label can be 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, or a number or a range between any of these values, of nucleotides in length. A set of molecular identifier labels 210 can contain 10, 20, 40, 50, 70, 80, 90, 10², 10³, 10⁴, 10⁵, 10⁶, 10⁷, 10⁸, 10⁹, 10¹⁰, 10¹¹, 10¹², 10¹³, 10¹⁴, 10¹⁵, 10²⁰, or a number or range between any of these values, molecular identifier labels 210. And the set of molecular identifier labels 210 can, for example, each contain a unique label region 214. The labeled cDNA molecules 204 can be purified to remove excess molecular identifier labels 210. Purification can comprise Ampure bead purification.

As shown in step 2, products from the reverse transcription process in step 1 can be pooled into 1 tube and PCR amplified with a 1^(st) PCR primer pool and a 1^(st) universal PCR primer. Pooling is possible because of the unique label region 214. In particular, the labeled cDNA molecules 204 can be amplified to produce nested PCR labeled amplicons 222. Amplification can comprise multiplex PCR amplification. Amplification can comprise a multiplex PCR amplification with 96 multiplex primers in a single reaction volume. In some embodiments, multiplex PCR amplification can utilize 10, 20, 40, 50, 70, 80, 90, 10², 10³, 10⁴, 10⁵, 10⁶, 10⁷, 10⁸, 10⁹, 10¹⁰, 10¹¹, 10¹², 10¹³, 10¹⁴, 10¹⁵, 10²⁰, or a number or a range between any of these values, multiplex primers in a single reaction volume. Amplification can comprise 1^(st) PCR primer pool 224 of custom primers 226A-C targeting specific genes and a universal primer 228. The custom primers 226 can hybridize to a region within the cDNA portion 206′ of the labeled cDNA molecule 204. The universal primer 228 can hybridize to the universal PCR region 216 of the labeled cDNA molecule 204.

As shown in step 3, products from PCR amplification in step 2 can be amplified with a nested PCR primers pool and a 2^(nd) universal PCR primer. Nested PCR can minimize PCR amplification bias. In particular, the nested PCR labeled amplicons 222 can be further amplified by nested PCR. The nested PCR can comprise multiplex PCR with nested PCR primers pool 230 of nested PCR primers 232A-C and a 2^(nd) universal PCR primer 228′ in a single reaction volume. The nested PCR primer pool 228 can contain 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, or a number or range between any of these values, different nested PCR primers 230. The nested PCR primers 232 can contain an adaptor 234 and hybridize to a region within the cDNA portion 206″ of the labeled amplicon 222. The universal primer 228′ can contain an adaptor 236 and hybridize to the universal PCR region 216 of the labeled amplicon 222. Thus, step 3 produces adaptor-labeled amplicon 238. In some embodiments, nested PCR primers 232 and the 2^(nd) universal PCR primer 228′ cannot contain the adaptors 234 and 236. The adaptors 234 and 236 can instead be ligated to the products of nested PCR to produce adaptor-labeled amplicon 238.

As shown in step 4, PCR products from step 3 can be PCR amplified for sequencing using library amplification primers. In particular, the adaptors 234 and 236 can be used to conduct one or more additional assays on the adaptor-labeled amplicon 238. The adaptors 234 and 236 can be hybridized to primers 240 and 242. The one or more primers 240 and 242 can be PCR amplification primers. The one or more primers 240 and 242 can be sequencing primers. The one or more adaptors 234 and 236 can be used for further amplification of the adaptor-labeled amplicons 238. The one or more adaptors 234 and 236 can be used for sequencing the adaptor-labeled amplicon 238. The primer 242 can contain a plate index 244 so that amplicons generated using the same set of molecular identifier labels 208 can be sequenced in one sequencing reaction using NGS.

In the methods disclosed herein, stochastic barcoding can be used for molecular indexing. Methods and techniques for stochastic barcoding have been described, for example, in U.S. Patent Pub. No. 20150299784, U.S. Pat. No. 8,835,358, and Fu et al., PNAS, 2001 108(22):9026-9031, the content of which is incorporated herein by reference in its entirety. Non-limiting exemplary methods for performing single molecule digital counting by the stochastic labeling of a collection of identical molecules are illustrated in FIGS. 3 and 4A-B.

Some embodiments of stochastic barcoding are illustrated schematically in FIG. 3. The library of different label-tag sequences 301 is combined with a sample that includes an unknown number of targets of interest 303. Three different species of target are shown, 303 a, 303 b and 303 c, present at 4, 6 and 3 copies respectively. The individual label-tag oligonucleotides from library 301 are covalently attached to the different targets to form target-label-tag molecules 305. Each target has a collection of different label-tag molecules 305 a, 305 b and 305 c and within each target-specific collection the members differ in the label-tag oligo that is attached. On the array 307, each target is tiled in combination with all possible label-tag combinations represented with each different combination being present at a different known or determinable location on the array. In the figure each different possible combination of target and label-tag is represented by a single probe for illustration purposes, but on the array each different probe is preferably present in a feature having multiple copies of the same probe sequence. The array is divided into subarrays 307 a, 307 b and 307 c for illustrative purposes. The upper portion 309 of the probes varies at each feature according to the different label-tag. The lower portion 313 is the same for all features of each subarray and is complementary to the target. After hybridization individual features of the array are labeled through hybridization of the complementary target-label-tag molecule to the feature. The figure shows a detectable label 311 may be used to detect features where a target-label-tag is hybridized.

Labels or counters 301 are combined with assay targets 303 so that each target is combined with one label to form label-targets 305. The process of combining an individual target with individual label molecules is a stochastic process. The number of labels each target type combines with is directly proportional to the number of individual targets of that target type or the copy number of the target. The number of labels is counted by hybridization to arrays where individual label-targets are detected at different features.

Each copy of a molecule (from a collection of identical target molecules 303) randomly captures a label by choosing from a large, non-depleting reservoir of diverse labels 301. The uniqueness of each labeled molecule is governed by the statistics of random choice, and depends on the number of copies of identical molecules in the collection compared to the diversity of labels. Once the molecules are labeled each has been given a unique identity and can now be separately detected. In some embodiments, it is preferable to first amplify the labeled targets prior to detection so that simple present/absent threshold detection methods can be used. Counting the number of labels is used to determine the original number of molecules in solution. In some embodiments, the molecules to be counted are each members of a class that shares some common feature, for example, they may each be a single copy of a particular gene sequence or nucleic acid sequence. Counting may be applied, for example, to mRNA targets, splice products, alternatively spliced products, structural RNAs, tRNA, miRNA, siRNA, microRNA and the like. Similarly, counting may be applied to DNA, for example, gene copy number, chromosome number, mitochondrial DNA, bacterial genomes, pathogen nucleic acid, viral nucleic acids and the like. Counting may be applied in research of disease in humans or other mammals or agricultural organisms, e.g. cattle, chicken, wheat, rice, fish, etc. Counting may also be applied to counting aspects of microbes, such as environmental measurements, e.g. water quality testing. The methods may be particularly useful where small numbers of items are to be counted and an accurate count is desirable rather than a relative estimate.

The targets are mixed with a collection of label-tag sequences, each label-tag being a different sequence and the collection having a number that is preferably 10 times the number of copies of the most abundant target to be counted. In some embodiments, the label-tags are a collection of known sequences such as a collection of all possible 6mers (N6). Each of the label-tag sequences is present in multiple copies in the mixture, but all are present at approximately equal amounts. The label-tag sequences are ligated to the targets. Ligation is random so that any given label-tag has about the same probability of ligating to any one target occurrence. So if there are 1000 different targets each could be ligated to a different label-tag sequence and the probability that any two target occurrences will have the same label-tag ligated is low. Because the ligation is a random stochastic process there is a known probability that if there are C copies of a given target and N different label-tags that any two copies of a target T will have the same label.

T1, T2, . . . TN, C1, C2, . . . CX, L1, L2, . . . LY where T are the different targets and there are N different targets, C are the different copies of a target and there are X copies of that target and L are the different label label-tags and there are Y label tags. X varies for each target and determining X is one of the objects of the present invention. The relationship between X and Y determines the probability that two C's will have the same L. In some embodiments, Y is greater than X for each target to be counted. This reduces the probability of undercounting due to double labeling. If C1 and C2 of T1 are both labeled with L3 both copies will be counted as a single occurrence, resulting in under counting. Undercounting can also be adjusted for by estimating the number of copies that are likely to be multiply labeled and adjusting the final count upwards to take those into account. For example, if there is a likelihood that 5 of 1000 copies will be labeled with the same label tag then the final number should be adjusted up by 0.5%.

In some embodiments, the detection is by hybridization to an array of probes. The array has a collection of features for each target that includes a different feature for each label tag. For example, if there are X label tags there are X features for each target, T1L1, T1L2, . . . T1LX and the same for target 2, T2L1, T2L2, . . . T2LX, out to TNL1, TNL2, . . . TNLX. The number of features of the array is on the order of X times N. Each probe has a target complementary sequence and a label tag complementary sequence. Within a set of probes for a given target the target segment of the probe would remain constant and the label tag portion varies from feature to feature so that each label tag sequence is represented by at least one feature for each target.

In some embodiments, the methods may be used to count the number of copies of each of a plurality of targets in a sample. The amount of target containing sample mixed with the label tags may be diluted so that the number of copies of each target to be counted is less than the number of label tags. For example, if the targets to be counted are present at about 1000 copies per cell and there are 10000 label tags you want to have the amount of sample in the mixture to be about the equivalent of one cell's worth of RNA. You can mix that with multiple copies of each label-tag, but you want to keep the absolute number of copies of target below the number of types of label tag sequences. Dilution of the sample and use of an appropriately small amount of starting material may be used. If a target sequence is present at low copy number per cell it is possible to use the nucleic acid from a larger number of cells. For example, to measure the DNA copy number of a chromosomal region relative to other chromosomal regions the expected copy number is low (e.g. 2 for normal) so if there are 10000 different label tags, the number of genomes that can be added to the sample for attachment of label tags can be high, e.g. 500 to 1000.

In some embodiments, the methods are used to identify regions of genomic amplification and chromosomal abnormalities. For example, the methods may be used to detect trisomy. Most of the chromosomal regions will be present in 2 copies per cell and the region of trisomy will be present in 3 copies per cell. You would expect to observe a 3:2 ratio in your count. For example, if you have 500 genomes you would have 1000 copies of most regions and 1500 copies of the trisomy regions. Small errors in the counting, resulting from undercounting, would have little or no effect on the counting. In some embodiments, controls of known copy number may be spiked in to a sample to determine accuracy.

Stochastic labeling of t_(1,N) (collection of essential identical molecules of copy 1, 2 . . . N of target 1) by L_(1,m) (effectively an infinite reservoir of diversity m when m is much greater than N). This allows for complete or near complete resolution of members of t_(1,N), by imparting separate identities to the members of the collection of t_(1,N) (provided that m is sufficiently smaller than N in the labeling). This provides for a stochastic or random projection of t_(1,N) onto L_(1,m). In some embodiments L_(1,m) is a library and the members of the library that are associated with t_(1,N) can be counted to determine the number of copies of the target. In some embodiments the methods can be described as indexing the members of the target. This provides a method to follow individual molecules that are members of a type of molecule that would not otherwise be distinguishable one from another.

Because stochastic labeling can impart identifiability to otherwise non-identifiable molecules it can impart identifiability to any two targets that may be very similar, but different. Examples of targets that may be highly similar but could be separately counted using the disclosed methods, include, for example, alternative splice forms of a gene, and sequences that have one or more variations, including a variation in a single base (e.g. SNP or indels (insertion or deletions of short regions, e.g. 1-5 bases). In some embodiments the methods impart a clonal labeling, that allows a single copy to be separately detected and separately isolated from the solution.

Some nucleic acid sequencing reactions use methods that stochastically attach targets to a solid support followed by amplification of the attached target and analysis. The target attaches in an unknown location and the location can be determined by sequencing the amplified target at specific locations. In contrast, the disclosed methods provide for clonal amplification of known targets in a known location. The stochastic nature of the formation of the target-label-tag molecule provides a mechanism for isolating single occurrences of selected targets that can be subsequently amplified and analyzed. In some embodiments the label can be used as a handle for isolating clonal populations of targets. The labeling step generates an indexed library that has a variety of applications. For example, the indexed library could be used for sequencing applications. The method adds distinguishability to any set of molecules, even molecules that are not distinguishable by other mechanisms because they may share common regions or even been identical. The indexed library can be stored and used multiple times to generate samples for analysis. Some applications include, for example, genotyping polymorphisms, studying RNA processing, and selecting clonal representatives to do sequencing.

The methods may be used to convert an analog readout of hybridization signal intensities on arrays into a measurable process that can be scored digitally on the arrays. The method leverages a random process where the tagging of assayed molecules is governed by stochastic behavior. In a random process, the more copies of a given target, the greater the probability of being tagged with multiple labels. A count of the number of incorporated labels for each target can approximate the abundance level of a given target of interest. The ability to count labels on microarrays would be a clear cost-advantage over the other existing techniques.

A stochastic counting assay system as described herein can also be a sub-system within a much larger bio-analysis system. The bio-analysis system could include all the aspects of sample preparation prior to, for example, optical detection, the post processing of data collected in the optical detection phase and finally decision making based on these results. Sample preparation may include steps such as: extraction of the sample from the tested subject (human, animal, plant environment etc.); separation of different parts of the sample to achieve higher concentration and purity of the molecules under investigation; sample amplification (e.g. through PCR); attachment of fluorescence tags or markers to different parts of the sample; and transfer of the sample or a portion of the sample into a reaction vessel or site on a substrate. The post processing of the collected data may include: normalization; background and noise reduction; and statistical analysis such as averaging over repeated tests or correlation between different tests. The decision making may include: testing against a predefined set of rules and comparison to information stored in external data-bases.

The applications and uses of the stochastic labeling and counting methods and systems described herein can produce one or more result useful to diagnose a disease state of an individual, for example, a patient. In some embodiments, a method of diagnosing a disease comprises reviewing or analyzing data relating to the presence and/or the concentration level of a target in a sample. A conclusion based review or analysis of the data can be provided to a patient, a health care provider or a health care manager. In some embodiments the conclusion is based on the review or analysis of data regarding a disease diagnosis. It is envisioned that in another embodiment that providing a conclusion to a patient, a health care provider or a health care manager includes transmission of the data over a network.

Applications for the disclosed methods include diagnosing a cancerous condition or diagnosing viral, bacterial, and other pathological or non-pathological infections. Additional applications of the disclosed methods and systems include, pathogens detection and classification; chemical/biological warfare real-time detection; chemical concentration control; dangerous substance (e.g., gas, liquid) detection and alarm; sugar and insulin levels detection in diabetic patients; pregnancy testing; detection of viral and bacterial infectious diseases (e.g. AIDS, Bird Flu, SARS, West Nile virus); environmental pollution monitoring (e.g., water, air); and quality control in food processing.

Any available mechanism for detection of the labels may be used. While many of the embodiments discussed above use an array readout form, it will be obvious to one of skill in the art that other methods for readout may be used. For example, sequencing may be used for detection of the labels in some embodiments.

In some embodiments the readout is on an array. The array may be a solid support having immobilized nucleic acid probes attached to the surface in an ordered arrangement. The probes may be, for example, synthesized in situ on the support in known locations using photolithography or the probes may be spotted onto the support in an array format. As discussed above, in some embodiments the array includes a probe feature for each possible label-target combination. A feature preferably includes many copies of a single probe sequence. The feature may also have some probes that are not full length, resulting from truncation of synthesis. The photo activation process may not be 100% efficient so some probes are terminated at each step without having subsequent bases added. These truncated probes have the sequence of a portion of the full length probe.

Sequencing readout. After attachment of the labels to the targets in a stochastic manner, the targets may be amplified according to any of the methods disclosed herein and the amplification product may be subjected to any available sequencing method.

A number of alternative sequencing techniques have been developed and many are available commercially. These include the use of microarrays of genetic material that can be manipulated so as to permit parallel detection of the ordering of nucleotides in a multitude of fragments of genetic material. The arrays typically include many sites formed or disposed on a substrate. Additional materials, typically single nucleotides or strands of nucleotides (oligonucleotides) are introduced and permitted or encouraged to bind to the template of genetic material to be sequenced, thereby selectively marking the template in a sequence dependent manner. Sequence information may then be gathered by imaging the sites. In certain current techniques, for example, each nucleotide type is tagged with a fluorescent tag or dye that permits analysis of the nucleotide attached at a particular site to be determined by analysis of image data.

In some embodiments, mass spectrometry analysis may be used to detect the labels and count the targets. The labels can be distinguishable based on size or other property that can be detected. Many of the examples provided herein identify the label based on unique nucleic acid sequence but any distinguishable label may be used, for example, the pool of labels may be labels that are differentially detectable based on fluorescence emission at a unique wavelength.

FIG. 4A illustrates the attachment of different labels from the pool 301 to each of 4 different copies of the same target “t.” Label 20 is attached to t1, label 107 to t2, label 477 to t3, and label 9 to t4. The labeled targets are then amplified to generate four unique populations, each population representing a single occurrence of the target in the starting sample.

FIG. 4B illustrates the method for a comparison of two samples, sample 1 and 2. The target 401 Gene A is present in 2 copies in sample 1 and 9 copies in sample 2. Both samples have non-target molecules 403. The labels 405 are combined with the samples and target molecules are attached to individual label-tag molecules in a stochastic manner. The targets with attached label-tags are hybridized to an array 411 having many features, there is a feature for each possible target-label-tag combination. Some of the features are labeled, for example, 409 and others are not, for example, 407. The labeled features indicate the presence of a specific target-label-tag combination and each corresponds to a count. As shown for gene A in sample 1 there are two labeled features so the count is 2. For Gene A in sample 2 there are 9 labeled features so the count is 9.

In some embodiments, the methods disclosed herein can stochastically expand a population of indistinguishable molecules to a population of uniquely identifiable and countable molecules. High-sensitivity threshold detection of single molecules is demonstrated, and the process can be used to count both the absolute and relative number of molecules in a sample. The method should be well suited for determining the absolute number of multiple target molecules in a specified container, for example in high-sensitivity clinical assays, or for determining the number of transcripts in single cells. The approach should also be compatible with other molecular assay systems. For example, antibodies could be stochastically labeled with DNA fragments and those that bind antigen harvested. After amplification, the number of labels detected will reflect the original number of antigens in solutions. In the examples shown here, DNA is used because of the great diversity of sequences available, and because it is easily detectable. In principle, any molecular label could be used, for example fluorescent groups or mass spectroscopy tags, as long as they are easily detected and they have sufficient diversity for the desired application. Although many of the examples refer to populations

Methods and compositions for single molecule counting employing the use of stochastic labeling are disclosed herein. In some embodiments, a diverse set of labels is randomly attached to a population of identical molecules is converted into a population of distinct molecules suitable for threshold detection. Random attachment as used herein refers to a process whereby any label can be attached to a given molecule with the same probability. To demonstrate stochastic labeling methods experimentally the absolute and relative number of selected genes were determined after stochastically labeling about 1000, 2000, 3000, 4000, 5000, 6000, 7500, 10000, 20000, 30000, 40000, 50000, 60000, 75000, 100000, 200000, 300000, 400000, 500000, 600000, and 750000 different fragments and more than 1000000 fragments of a genome such as a human genome. The approach does not require the physical separation of molecules and may take advantage of highly parallel methods such as microarray and sequencing technologies to simultaneously count absolute numbers of multiple targets. In some embodiments, stochastic labeling may be used for determining the absolute number of RNA or DNA molecules within single cells.

The methods disclosed herein may be used to take quantitative measurements of copies of identical molecules in a solution by transformation of the information to a digital process for detecting the presence of different labels. The stochastic properties of the method have been measured, and the relative and absolute digital counting of nucleic acid molecules is demonstrated. The method is extremely sensitive, quantitative, and can be multiplexed to high levels. In some embodiments a microarray-based detection method is used, but the method is extendable to many other detection formats.

In some embodiments, the methods are based on probability theory, where the outcome of chemical reactions occurring between a set of labeling molecules and a set of target molecules is modeled and tested. When all of the molecules in a uniform mixture of fixed volume collide and react randomly, the chemical events follow a stochastic process governed in part by the molecule concentration of each species.

Methods for analyzing genomic information often utilize a correlation between a measurement of the amount of material associated with a location. The location can be, for example, a feature of an array that contains a specific sequence that is known or can be determined or any type of solid support such as a bead, particle, membrane, etc. A common aspect to these methods is often hybridization of a target to be measured to a complementary probe attached to the solid support. The probe may be, for example, an oligonucleotide of known or determinable sequence, but may also be BACs, PACs, or PCR amplicons.

Because of the density of different features that can be obtained using synthesis methods such as photolithography, microarrays can be applied to high density applications. For example, at feature sizes of 1 micron square an array can have about 10⁸ features per cm². Within a feature, depending on the chemistry used for synthesis, the probes are spaced typically at about 10 nm spacing resulting in about 10⁴ molecules in a micron². At approximately full saturation about 10% of those probes are hybridized with target. There are then about 640 functional molecules in an array having 1 micron² spacing between features (˜800 nm² functional area). This relatively small number of functional molecules in a feature limits the dynamic range for estimating relative concentration from hybridization signal intensity.

For example, at feature sizes of 1 micron square an array can have about 10, 10², 10³, 10⁴, 10⁵, 10⁶, 10⁷, 10⁸, 10⁹, 10¹⁰, 10¹¹, 10¹², 10¹³, 10¹⁴, 10¹⁵, 10¹⁶, 10¹⁷, 10¹⁸, and 10¹⁹ features and more than 10²⁰ features per cm². Within a feature, depending on the chemistry used for synthesis, the probes are spaced at about 0.00001, 0.0001, 0.001, 0.01, 0.1, 0.5, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 75 nm spacing and more than 100 nm spacing. There may be about 10, 10², 10³, 10⁴, 10⁵, 10⁶, 10⁷, 10⁸, and 10⁹ molecules and more than 10¹⁰ molecules in one micron².

Methods are disclosed herein to overcome the dynamic range limitations observed with small feature sizes and small numbers of molecules on the array surface, by using a counting or digital readout as a substitute for the typical analog signal resulting from array hybridization.

Methods that use signal intensity to estimate relative concentrations of targets typically label the targets with a detectable label, often after an amplification step, and through hybridization of the labeled target to the probe, the probe and thus the feature is also labeled. The amount of label is detected and correlated with a measurement of the amount of target in the sample. The estimate of amount of a given target in a sample is typically relative to other targets in the sample or to previously obtained measurements and may be based on comparison to targets present in the sample at known or expected levels or to controls within the sample. This type of analysis can and has been used successfully, for example, to estimate genomic copy number to detect copy number variation in individuals or in cell populations.

Correlating intensity of hybridization signal or signal intensity with concentration of target molecules has limitations and can typically provide only an estimate of the absolute amount of a target, and may not be an accurate count of the actual amount of target present. The estimate may be an under or over estimate, particularly when comparing different targets or different samples. This is the result of many different factors, including but not limited to, differences between probes, feature specific effects, sample specific effects, feature size (as it decreases the ability to correlate accurately decreases) and experimental variation. Much of this variation can be addressed by data analysis methods, but the methods do not provide counting of individual molecules or events and are therefore subject to estimation errors.

In some embodiments, methods are disclosed for attaching a different label-tag sequence to each molecule of a particular target sequence or more preferably a collection of target sequences of interest. For example, a sample having 100 molecules of target type 1 is mixed with an excess, for example, 1000 different label-tag sequences, forming a library of label-tag sequences under ligation conditions. Multiple copies of the library of label-tag sequences are added so there are preferably many copies of each label-tag. Different label-tag sequences from the library are appended to each of the 100 target molecules so that each of the 100 molecules of the first target sequence has a unique label-tag sequence appended thereto. This results in 100 different target-label-tag combinations. The target-label-tag molecules may then be amplified to enrich the target-label-tag products relative to other non-targets. Amplification after labeling alters the absolute amount of the target, but because each occurrence in the original sample has been uniquely labeled this will not alter the count. The amplified target-label-tag products, whether amplified or not, can then be labeled with a detectable label, and hybridized to an array of probes. The features of the array that have target-label-tag hybridized thereto can be detected, for example, by labeling the hybridization complex with a fluorescent label and detecting the presence of signal at the features. In this example, because there are 1000 different labels possible and a single target being analyzed, there are 1000 different possible label-target sequences that might be generated so an array having a different feature for each of the 1000 different possibilities can be used. Assuming each target is labeled and no label is used twice, 100 of the 1000 different features should be detectable, indicating the corresponding label has been used.

Once the target molecules are labeled with the counter they can be amplified freely without impacting the counting since the readout is either yes, indicating detection or no indication not detected. In some embodiments, a simple detector having m elements for each target sequence can be constructed. The detector may be an array. An array having 10⁸ features or elements could assay 10⁵ different targets using 10³ different labels, for example. Other detection methods do not require individual elements for each counter, for example, sequencing.

In some embodiments, the “counter library” or “label-tag library” has approximately the same number of copies of each label-tag in the library. The label-tag sequences are not target specific, but are like the tags that have been used for other tagging applications, for example, the Affymetrix GENFLEX tag array. Possibly all label-tags in a set of label-tags will have similar hybridization characteristics so that the label-tags of the set can be detected under similar conditions.

For each target there are a series of features on the array, preferably one feature for each label-tag. In each of these features the portion of the probe that hybridizes to the target (or target complement) is the same but the label-tag complement is different in each feature. For example, to detect a first target RNA, “RNA1,” there would be a series of features each having a different probe (RNA1-tag1, RNA1-tag2, . . . RNA1-tagN). For each target to be detected there is a similar set of features, e.g. RNA2-tag1, RNA2-tag2, . . . RNA2-tagN. The set of label-tags is N tags and it is the unique combination of the label-tag with the target sequence that creates a novel sequence to be detected, for example, by hybridization.

Label-tag attachment to individual targets is a stochastic process whereby the probability of any given label-tag being attached to any target is stochastic. There is a random selection of label-tags by attaching the label-tags to the end of a known target sequence in a sequence independent manner. The label-tag is attached without requirement for it to hybridize to any portion of the target so there is no or minimal bias as to which label-tag sequence is attached. Individual molecules all look the same for the purpose of attachment of the label-tag.

The label-tag may be attached to the target by any method available. In some embodiments, the label-tag is attached by ligation of the label-tag to one of the ends of the target. In some embodiments, the probes of the array are complementary to a predicted junction between target and label so it is preferable that the labels are attached to all occurrences of a target at the same position. This is facilitated if the termini of each occurrence of a selected target are the same and are known. In some embodiments, target occurrences are fragmented with a restriction enzyme so that defined ends of known sequence are formed.

After label-tag attachment in some embodiments the target-label-tag segment is amplified. Attachment of universal primers to either end followed by PCR amplification is one method for amplifying. The universal primers may be added along with the label or at a subsequent ligation step.

For RNA targets an RNA ligase, such as T4 RNA ligase may be used. T4 RNA ligase 1 catalyzes the ligation of a 5′ phosphryl-terminated nucleic acid donor to a 3′ hydroxyl-terminated nucleic acid acceptor. Substrates include single-stranded RNA and DNA. RNA targets may also be circularized and used as template for rolling circle amplification using an enzyme having reverse transcriptase activity. T4 RNA ligase 1 may be used for circularization of RNA by ligating the ends of the molecule together. T4 RNA ligase 1 can also be used to ligate RNA to DNA.

Full-length mRNA can be selected by treating total or poly(A) RNA with calf intestinal phosphatase (CIP) to remove the 5′ phosphate from all molecules which contain free 5′ phosphates (e.g. ribosomal RNA, fragmented mRNA, tRNA and genomic DNA). Full-length mRNAs are not affected. The RNA can them be treated with tobacco acid pyrophosphatase (TAP) to remove the cap structure from the full-length mRNA leaving a 5′-monophosphate. A synthetic RNA adapter can be ligated to the RNA population. Only molecules containing a 5′-phosphate, (i.e. the uncapped, full-length mRNAs) will ligate to the adapters. Preferably the adapter has a variable label sequence, and may also have a constant sequence for priming. Preferably, the constant sequence is 5′ of the variable sequence. In some embodiments, the adapter ligated mRNA may then be copied to form a first strand cDNA by, for example, random priming or priming using oligo dT. The cDNA may subsequently be amplified by, for example, PCR. T4 RNA ligase may also be used for ligation of a DNA oligo to single stranded DNA.

In some embodiments, the ligated target-label-tag molecule may be enriched in the sample relative to other nucleic acids or other molecules. This enrichment may be, for example, by preferentially amplifying the target-label-tag methods, using for example, a DNA or RNA polymerase, or by degrading non target-label-tag molecules preferentially.

In some embodiments, the target-label-tag molecule may be nuclease resistant while the unligated target and unligated label molecules may be nuclease sensitive. A nuclease can be added to the sample after ligation so that ligated target-label-tag molecules are not digested but non-ligated molecules are digested. For example, the targets may be resistant to a 5′ exonuclease (but not a 3′ exonuclease) while the labels are resistant to a 3′ exonuclease but not a 5′ exonuclease. Ligating target to label generates a molecule that is resistant to 5′ and 3′ exonuclease activity. After ligation the sample may be treated with a 5′ exonuclease activity, a 3′ exonuclease activity or both 5′ and 3′ exonuclease activities. Exo VII, for example degrades single stranded DNA from both the 5′ and 3′ ends so the sample could be treated with Exo VII after ligation to degrade molecules that are not ligation products.

In some embodiments, amplification may include a rolling circle amplification (RCA) step. The targets may be ligated so that they have a label and a universal priming (UP) sequence attached to the 5′ end of the targets. The UP-label-target is then ligated to form a circle. A primer complementary to the UP is then hybridized to the circles and extended using a strand displacing polymerase. The resulting amplification product contains multiple copies of the complement of the circle, UP-target-L.

In some embodiments, targets may be labeled in a copying step. For example, a primer having a 3′ target specific region and a 5′ variable label region may be hybridized to the targets, either RNA or DNA, and extended to create a single complimentary copy of the target. Each extension product will have a different label and the junction between the label and the target specific region is known. The extension may be performed in the presence of nuclease resistant nucleotides so that the extension product is resistant to nuclease but the unextended primers are not. After extension the reaction is treated with a 3′-5′ exonuclease activity to digest unextended primer. Exonuclease I, for example, removes nucleotides from single stranded DNA in the 3′ to 5′ direction and Exo III removes nucleotides from the 3′ termini of duplex DNA. Exonuclease T (or RNase T) is a single-stranded RNA or DNA specific nuclease that requires a free 3′ terminus and removes nucleotides in the 3′ to 5′ direction. The extension products are then detected by hybridization to probes that are complementary to the primers and include the unique label portion and the constant target specific portion. If the target is RNA it can be digested with RNase H after extension. The extension product may also be amplified before hybridization.

In some embodiments, the probability that any two targets are labeled with the same label may be decreased by using two or more labeling steps. For example, a first labeling step where each target has a label selected from a set of labels followed by a second labeling set using the same set of labels. The first labeling event will be independent of the second so the probability that the first and second labeling events will both be the same in two independent targets is the product of the probability of two targets having the same label in either step. If there are N possible labels, and the first target is labeled first with label N1 and then with label N4, the probability that a second target will be labeled also with N1 and then N4 is 1/N². So if there are 100 different labels, the probability that two targets will be labeled with the same label in the first round and the same label in the second round is 1/10000.

In some embodiments, a first round of labeling may be done with 16 probes (for example, all possible 2 base combinations) and then a second round of labeling is done using the same 16 probes. The chance of any one probe attaching to a given target occurrence in the first round is 1 out of 16, the chance that the same probe will attach to the second target is 1/16 and the chance that the same two probes will attach is 1/16×1/16 or 1/256.

In some embodiments, reversible terminators are used to add a sequence to the end of each target being counted. For example, a 6 base sequence may be added and the chance of two being the same is 1 in 4⁶ or 1 in 4096.

The methods disclosed herein can be used to measure random cell-to-cell variations in gene expression within an isogenic population of cells. Such variation can lead to transitions between alternative states for individual cells. For example, cell-to-cell variation in the expression of comK in B. subtilis has been shown to select cells for transition to the competent state in which genes encoding for DNA uptake proteins are expressed.

Focusing Methods

Methods disclosed herein can, in some embodiments, include enriching a sample comprising a plurality of cells for cells of interest to produce an enriched cell sample, wherein enriching the sample comprises focusing cells of interest in the sample; isolating one or more cells of interest in the enriched cell sample with a flow cytometer; and obtaining sequence information of one or more polynucleotides from each of the one or more isolated cells. Various focusing methods and techniques can be used, for example, hydrodynamic focusing, magnetic field focusing, electric field focusing, gravitational field focusing, optical field focusing, and any combination thereof.

In some embodiments, enriching the sample includes focusing cells of interest in the sample, for example, into a core stream of cells. Focusing cells of interest can, in some embodiments, include acoustic focusing. Acoustic focusing uses acoustic waves to control the movement of particles (e.g., cells) during analysis, for example uses ultrasonic radiation pressure (e.g., >2 MHz) to transport particles into the center of a sample stream. In some embodiments, it is advantageous to combine acoustic focusing and hydrodynamic focusing to narrow core stream and uniform laser illumination. In some embodiments, acoustic focusing includes: suspending the plurality of cells in the sample in an elongated fluid-filled channel; and exposing said channel to an axial acoustic standing wave field parallel to the direction of flow, wherein said axial acoustic standing wave field drives the plurality of cells to positions of force minimum along the center axis of said channel to result in uniformly spaced cells.

Acoustic radiation pressure can manipulate cells. Applying an acoustic wave field generates a quasi-one-dimensional force field that focuses cells, for example, cells of interest. Acoustic focusing, alone or in combination with hydrodynamic focusing, can focus cells into a core stream of cells, for example, at positions of force potential minimum along the center axis of an elongated fluid-filled channel. Acoustic focusing thus increases the concentration of cells within the core stream. After discarding some or all of the fluid not within the core stream, the concentration of cells increases. Thus, acoustic focusing increases sample throughput of dilute samples with low concentration of cells of interest by increasing concentration of cells of interest.

FIGS. 5 and 6A-C illustrate non-limiting embodiments of the present disclosure that focus particles, such as cells of interest in a sample, into a core stream prior to isolating one or more cells of interest. Methods that can be used to focus the particles include acoustic focusing. As shown in FIGS. 5 and 6A, by applying acoustic forces 500A-B generated by an acoustic force generator, such as ultrasonic transducers 502A-B, to cells of interest 504 in a first section 506 of a channel 508, the cells of interest 504 are focused into a core stream 510. The channel 508 can be elongated and fluid-filled.

The cells of interest 504 can be focused into the core stream 510 at positions of force potential minimum along the center axis of the channel to result in uniformly spaced cells. The acoustic forces can comprise an axial acoustic standing wave field parallel to the direction of flow 510 within the channel 508. The ultrasonic transducers 502A-B create a standing wave field in the channel 508. Ultrasonic radiation pressure forces the cells of interest 504 into the core stream 512. After the cells of interest 504 are focused into the core stream 512, some or all of fluid not within the core stream 512 can be discarded through exits 514A-B. So the concentration of cells in the second section 516 of the channel 508 is higher than the concentration of cells in the first section 506 of the channel 508. In some embodiments of the present disclosure, it is advantageous to focus the cells into the core stream 512. In other embodiments of the present disclosure, it is advantageous to focus the cells to positions of force potential minimum in the core stream 510 along the center axis of the channel to result in uniformly spaced cells.

FIG. 7 illustrates embodiments of the present disclosure that focus particles each having a size within a predetermined range in a sample, such as cells of interest each having a size within a predetermined range in a sample, into a core stream prior to isolating one or more cells of interest. Methods that can be used to focus the particles with sizes within a predetermined range include acoustic focusing. As described in connection with FIGS. 5 and 6A, by applying acoustic forces 500A-B generated by an acoustic force generator, such as ultrasonic transducers 502A-B, to the cells of interest each having a size within a predetermined range 704 in a first section 506 of a channel 508, the cells of interest each having a size within a predetermined range 704 are focused into a core stream 510. By adjusting parameters of the acoustic forces 500A-B, for example the frequency, magnitude, and waveform of the acoustic forces 500A-B, only cells of interest each having a size within a predetermined range 704 are focused into the core stream 512. Cells not of interest each having a size not within a predetermined range 705 are not focused into the core stream 512. The channel 508 can be elongated and fluid-filled.

The cells of interest having sizes within a predetermined range 704 can be focused into the core stream 510 at positions of force potential minimum along the center axis of the channel to result in uniformly spaced cells. The acoustic forces can comprise an axial acoustic standing wave field parallel to the direction of flow 510 within the channel 508. The ultrasonic transducers 502A-B create a standing wave field in the channel 508. Ultrasonic radiation pressure forces the cells of interest having sizes within a predetermined range 704 into the core stream 512. After the cells of interest that have certain sizes 704 are focused into the core stream 512, some or all of fluid not within the core stream 512 and cells not of interest having sizes not within a predetermined range 705 can be discarded through exits 514A-B. So the concentration of cells of interest that have sizes within a predetermined range 704 in the second section 516 of the channel 508 is higher than the concentration of cells of interest that have sizes within a predetermined range 704 in the first section 506 of the channel 508. In some embodiments of the present disclosure, it is advantageous to focus the cells of interest that have sizes within a predetermined range 704 into the core stream 512. In other embodiments of the present disclosure, it is advantageous to focus the cells of interest that have sizes within a predetermined range to positions of force potential minimum in the core stream 510 along the center axis of the channel to result in uniformly spaced cells.

There are many different arrangements where acoustic focusing can be advantageous. Referring to FIGS. 5 and 7, the acoustic field can be used in the channel 508 that can be circular, square, or any other geometry. One exemplary transducer is shown in FIG. 6B. In FIG. 6B, acoustic force 500 is reflected by the wall of the channel 508 shown as acoustic force 500′. Two transducers opposite of each other are shown in FIGS. 5, 6A, and 7. Two transducers orthogonal of each other are shown in FIG. 6C. Acoustic force 500A is reflected by the wall of the channel 508 shown as acoustic force 500A′. Acoustic force 500B is reflected by the wall of the channel 508 shown as acoustic force 500B′. There can be 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, or a number or range between any of these values, transducers 500. Using two transducers opposite of each other can provide feedback to monitor the acoustic field within the elongated fluid-filled channel 506. Additionally transducers can be attached in orthogonal directions to create force fields, for example axial acoustic standing wave fields, that are optimized for focusing cells of different types, sizes, and shapes. In some embodiments, serial acoustic focusing in one or more channels 508 focus the cells of interest 504 into a narrower core stream 512.

The numbers of exits shown in FIGS. 5 and 7 are two, including exits 514A and 514B. There can be 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, or a number or a range between any of these values, exits. The second section 516 shown in FIGS. 5 and 7 is narrower than the first section 506. In some embodiments, the second section 516 can be wider than the first section 506, or the two sections can have the same size. The two sections 506 and 516 can have the same shape or of different shapes. The two sections 506 and 516 can be made of the same materials or of different materials.

FIG. 8 illustrates embodiments of the present disclosure capable of combining acoustic focusing and hydrodynamic focusing to concentrate cells according to one embodiment of the present invention. A sample of cells 504 flow through a channel 508 that can be elongated and fluid-field. Sheath fluid 800 hydrodynamically focuses cells 504 to the central core stream 512 initially. Transducers 500A and 500B utilizes acoustic focusing to further focus cells 504 within the central core stream 512. After the cells 504 are focused into the core stream 512 by both hydrodynamic focusing and acoustic focusing, some or all of fluid not within the core stream 512 can be discarded through exits 514A-B. Consequently the concentration of cells in the second section 516 of the channel 508 is higher than the concentration of cells in the first section 506 of the channel 508.

In some embodiments, acoustic focusing, alone or in combination with hydrodynamic focusing, of the cells of interest at 112B increase the concentration of cells of interest by at least 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 10²%, 10³%, 10⁴%, 10⁵%, 10⁶%, 10⁷%, 10⁸%, 10⁹%, 10¹⁰%, 10¹¹%, 10¹²%, 10¹³%, 10¹⁵%, 10²⁰%, or a number or range between any two of these values. Acoustic focusing, alone or in combination with hydrodynamic focusing, of the cells of interest at 112B increases the speed of sorting at 116B by at least 10-folds, 20-folds, 30-folds, 40-folds, 50-folds, 60-folds, 70-folds, 80-folds, 90-folds, 10²-folds, 10³-folds, 10⁴-folds, 10⁵-folds, 10⁶-folds, 10⁷-folds, 10⁸-folds, 10⁹-folds, 10¹⁰-folds, 10¹¹-folds, 10¹²-folds, 10¹³-folds, 10¹⁵-folds, 10²⁰-folds, or a number or a range between any two of these values.

Depletion of Cells Not of Interest, Interfering Cells and Debris

In some embodiments, enriching the sample includes depleting cells not of interest in the sample. In some embodiments, enriching the sample includes both acoustic focusing and depleting cells not of interest in the sample. In some embodiments, one or more of cells not of interest, interfering cells and debris in the sample can be depleted, for example using magnetic depletion. According to some embodiments, depletion of cells not of interest, interfering cells, and debris (e.g., using magnetic depletion at 108B of FIG. 1B) can remove at least 1%, 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 95%, 99%, 99.5%, 99.9%, or a number or a range between any two of these values, of the cells not of interest, interfering cells, and/or debris originally present in the sample. In some embodiments, at least 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99%, 99.5%, 99.9%, 99.95%, 99.99%, 99.995%, 99.999%, or a number or a range between any two of these values, of the solid mass in the sample after depletion is the cells of interest. In some embodiments, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99%, 99.5%, 99.9%, 99.95%, 99.99%, 99.995%, 99.999%, or a number or a range between any two of these values, of the solid mass in the sample after depletion is the cells of interest. In some embodiments, the concentration of the cell of interest in the sample is increased by at least, or at least about 2-folds, 3-folds, 4-folds, 5-folds, 6-folds, 7-folds, 8-folds, 9-folds, 10-folds, 50-folds, 100-folds, 250-folds, 500-folds, 750-folds, 1000-folds, 5000-folds, 10000-folds, 50000-folds, 100000-folds, 500000-folds, 1000000-folds, or a number or a range between any two of these values, after depletion. In some embodiments, the concentration of the cell of interest in the sample is increased by, or by about, 2-folds, 3-folds, 4-folds, 5-folds, 6-folds, 7-folds, 8-folds, 9-folds, 10-folds, 50-folds, 100-folds, 250-folds, 500-folds, 750-folds, 1000-folds, 5000-folds, 10000-folds, 50000-folds, 100000-folds, 500000-folds, 1000000-folds, or a number or a range between any these two values, after depletion.

Cells of interest can be acoustically focused prior or subsequent to depleting cells not of interest in the sample. In some embodiments, the depletion is carried out by magnetic depletion. Magnetic depletion can, for example, utilize magnetic beads with antibodies, for example monoclonal antibodies and polyclonal antibodies, conjugated to the surfaces of the magnetic beads. The antibodies target detectable markers, for example cell surface markers, on cells not of interest and interfering cells. Examples of such magnetic beads include BD IMag™ particles. When a sample containing cells labeled with these magnetic beads is placed in a magnetic field created by a magnetic, for example BD IMagnet™, the magnetic field attracts the cells not of interest which are labeled to the magnet, allowing passage of cells of interest which are not unlabeled. Magnetic depletion allows rapid and effective negative selection of cells of interest. Cells not of interest which are labeled migrate toward the magnet creating the magnetic field, allowing cells of interest which are not labeled in suspension to pass through. Magnetic depletion can be repeated multiple times to increase the purity and concentration of the cells of interest. Magnetic depletion can utilize different types of magnetic beads that target different detectable markers to remove different types of cells not of interest, interfering cells, and debris.

To remove leukocytes, magnetic beads with monoclonal antibody conjugated to their surfaces can be used. These beads are optimized for the depletion of CD4-bearing leukocytes. The CD4 (L3T4) differentiation antigen can be expressed on most thymocytes, a subpopulation of mature T lymphocytes (i.e., MHC class II-restricted T cells, including most T helper cells), and a subset of nature killer T (NK-T) cells. In addition, CD4 can be expressed on pluripotent hematopoietic stem cells, bone marrow myeloid and B-lymphocyte precursors, intrathymic lymphoid precursors, and a subset of splenic dendritic cells.

Flow Cytometry

In some embodiments, isolating one or more cells of interest in the enriched cell sample can be performed with a flow cytometer. In some embodiments, the flow cytometer utilizes fluorescence-activated cell sorting.

Flow cytometry is a valuable method for the analysis and isolation of cells. As such it has a wide range of diagnostic and therapeutic applications. Flow cytometry utilizes a fluid stream to linearly segregate cells such that they can pass, single file, through a detection apparatus. Individual cells can be distinguished according to their location in the fluid stream and the presence of detectable markers. Cells flow through the focused interrogation point where at least one laser directs a laser beam to a focused point within the channel. The sample fluid containing cells is hydrodynamically focused to a very small core diameter by flowing sheath fluid around the sample stream at a very high volumetric rate. The small core diameter can be fewer than 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200 micrometers, or a number or a range between any two of these values. The volumetric rate of the sheath fluid can be on the order of at least 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 200, 300, 400, 500, 600, 700, 800, 900, or 1000 times, or a number or a range between any two of these values, the volumetric rate of the sample. This results in very fast linear velocities for the focused cells on the order of meters per second. So each cell spends a very limited time in the excitation spot, for example fewer than 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100 microseconds, or a number or a range between any two of these values. Once the cells pass the interrogation point the cells cannot be redirected to the interrogation point again because the linear flow velocity cannot be reversed.

Flow cytometers are analytical tools that enable the characterization of cells on the basis of optical parameters such as light scatter and fluorescence. In a flow cytometer, cells in a fluid suspension are passed by a detection region in which the cells are exposed to an excitation light, typically from one or more lasers, and the light scattering and fluorescence properties of the cells are measured. Cells or components thereof typically are labeled with fluorescent dyes to facilitate detection. A multiplicity of different cells or components can be simultaneously detected by using spectrally distinct fluorescent dyes to label the different cells or components. In some implementations, a multiplicity of photodetectors, one for each of the scatter parameters to be measured, and one for each of the distinct dyes to be detected are included in the analyzer. The data obtained comprise the signals measured for each of the light scatter parameters and the fluorescence emissions.

Isolation of biological cells has been achieved by adding a sorting or collection capability to flow cytometers. Cells in a segregated stream, detected as having one or more desired characteristics, are individually isolated from the sample stream by mechanical or electrical removal. This method of flow sorting has been used to sort cells of different types, to separate sperms bearing X and Y chromosomes for breeding, to sort chromosomes for genetic analysis, and to isolate particular organisms from complex biological populations.

A common flow sorting technique utilizes drop sorting in which a fluid stream containing linearly segregated cells is broken into drops and the drops containing cells of interest are electrically charged and deflected into a collection tube by passage through an electric field. Drop sorting systems are capable of forming drops at a rate of about 100, 500, 1000, 2000, 3000, 4000, 5000, 6000, 7500, 10000, 20000, 30000, 40000, 50000, 60000, 75000, 100000, 200000, 300000, 400000, 500000, 600000, 750000, 1000000 drops/second, or a number or a range between any two of these values, in a fluid stream that is passed through a nozzle having a diameter less than 1000, 750, 600, 500, 400, 300, 200, 100, 75, 60, 50, 40, 30, 20, 10, 5, 2, 1 micrometers, or a number or a range between any two of these values. Drop sorting requires that the drops break off from the stream at a fixed distance from the nozzle tip. The distance is normally on the order of a few millimeters from the nozzle tip and can be maintained for an unperturbed fluid stream by oscillating the nozzle tip at a predefined frequency.

The linearly segregated cells in the stream can be characterized as they pass through an observation point situated just below the nozzle tip. Once a cell is identified as meeting about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 75, 100, 200, 300, 400, 500, 600, 750 criteria, or a number or a range between any two of these values, or more than 1000 criteria, the time at which it will reach the drop break-off point and break from the stream in a drop can be predicted. Possibly, a brief charge is applied to the fluid stream just before the drop containing the selected cell breaks from the stream and then grounded immediately after the drop breaks off. The drop to be sorted maintains an electrical charge as it breaks off from the fluid stream, and all other drops are left uncharged. The charged drop is deflected sideways from the downward trajectory of the other drops by an electrical field and collected in a sample tube. The uncharged drops fall directly into a drain.

Cytometers can further comprise means for recording the measured data and analyzing the data. For example, data storage and analysis can be carried out using a computer connected to the detection electronics. For example, the data can be stored in tabular form, where each row corresponds to data for one cell, and the columns correspond to each of the measured parameters. The use of standard file formats, such as an “FCS” file format, for storing data from a flow cytometer facilitates analyzing data using separate programs and/or machines. Using current analysis methods, the data typically are displayed in 2-dimensional (“2D”) plots for ease of visualization, but other methods can be used to visualize multidimensional data.

The parameters measured using a flow cytometer typically include the excitation light that is scattered by the cell along a mostly forward direction, referred to as forward scatter (“FSC”), the excitation light that is scattered by the cell in a mostly sideways direction, referred to as side scatter (“SSC”), and the light emitted from fluorescent molecules in one or more channels (range of frequencies) of the spectrum, referred to as FL1, FL2, etc., or by the fluorescent dye that is primarily detected in that channel. Different cell types can be identified by the scatter parameters and the fluorescence emissions resulting from labeling various cell proteins with dye-labeled antibodies.

Fluorescence-activated cell sorting or is a specialized type of flow cytometry. It provides a method for sorting a heterogeneous mixture of cells into two or more containers or wells of a microtiter plate, one cell at a time, based upon the specific light scattering and fluorescent characteristics of each cell. It records fluorescent signals from individual cells, and physically separates cells of particular interest. The acronym FACS is trademarked and owned by Becton Dickinson.

The cell suspension is placed near the center of a narrow, rapidly flowing stream of liquid. The flow is arranged so that on the average (Poisson distribution) there is a large separation between cells relative to their diameter. A vibrating mechanism causes the stream of cells to break into individual droplets. The system is adjusted so that there is a low probability of more than one cell being in a droplet. Just before the stream breaks into droplets the flow passes through one or more laser intersects where the fluorescent character of interest of each cells are measured. If a cell is to be collected, a charge is applied to the flow cell during the period of time one or more drops form and break off from the stream. These charged droplets then fall through an electrostatic deflection system that diverts droplets into target containers based upon the charge applied to the droplet.

FIG. 9 shows a functional block diagram for one example of a sorting device, for example a flow cytometer, suitable to use for cell sorting, for example at 116B of FIG. 1B. The sorting device illustrated in FIG. 9 can be referred to as stream-in-air sorting system. Cells in sample stream 902 are shown. The sample stream 902 passes through an orifice 904 of a nozzle 906. The sample stream 902, upon exiting the nozzle 906 forms a jet 908. A laser beam 910 is generated by a laser 911 to illuminate the cells that can be included in the jet 908. The light is detected by a detection station 912 to generate multiple signals that are processed to generate a multi-parameter event data point. The generated event data can be provided to a sample behavior analyzer. The providing can be direct, via communication intermediaries (e.g., network/cloud), via memory, or a hybrid configuration.

Based on the values of the multiple signals, prior to droplets leaving jet 908, they are positively charged, negatively charged, or left neutral. Some droplets will include a cell of the sample as shown by a droplet 930 while other droplets will not include a cell of the sample as shown by a droplet 940. Droplets pass through a deflection field 914. The deflection field 914 includes two oppositely charged deflection plates 916 a and 916 b. The deflection plates 916 a and 916 b are configured to steer charged droplets in the jet 908 to their respective collection vessels 918 a, 918 b, or 918 c. As shown, vessel 918 b collects negatively charged droplets because the positive deflection plate 916 a will attract negatively charged droplets. Similarly, vessel 918 c will collect positively charged droplets because the negatively charged deflection plate 916 b will attract positively charged droplets. Each collection vessel can be a microtiter plate or a well of a microtiter plate.

In one collection scheme, the flow system identifies all cells of interest as they pass the detection station 912 based on the values of their signals, and then causes jet 908 to be charged or neutral at the instant the cell of interest leaves jet 908 as a droplet. In this way, cells of interest are caused to have the same charge. This allows collection of the cells in the same collection vessel, including the same or different wells of a multitier plate.

Non-limiting examples of the sorting device illustrated in FIG. 9 suitable to use for cell sorting, for example at 116B of FIG. 1B, include a BD FACSJazz™ cell sorter, a BD FACSseq™ cell sorter, or any other cell sorters including a Bio-Rad Laboratories, Inc. (Hercules, Calif.) S3e™ Cell Sorter, a Sony Biotechnology Inc. (San Jose, Calif.) SH800 cell sorter, a Beckman Coulter Inc. (Brea, Calif.) MoFlo™ XDP cell sorter.

Sorting Speed

In some embodiments, cells of interest can be sorted into one or more microtiter plates (i.e., isolated from the cell sample) (e.g., at 116B of FIG. 1B) in less than about 15 minutes, allowing time course data to be collected. Cells of interest in a sample can also be sorted in less than 0.01, 0.05, 0.1, 0.5, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 45, 50, 55, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 175, 200, 250, 300, 400, 500, 600, 750, 1000 minutes, or a duration or range between any of these two values.

The low-cost, high-throughput, single-cell genomic sequencing methods disclosed herein are fast and have high success rate. For example, the method can sort cells to obtain 96, 324, 946, or more cells of interest in less than about 0.1, 0.5, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 45, 50, 55, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 175, 200, 250, 300, 400, 500, 600, 750, 1000 minutes, or a duration or range between any of these two values. In some embodiments, 10³, 10⁴, 10⁵, 10⁶, 10⁷, 10⁸, 10⁹, 10¹⁰, 10¹¹, 10¹², 10¹³, 10¹⁴, 10¹⁵, 10¹⁶, 10¹⁷, 10¹⁸, 10¹⁹, 10²⁰ cells, or a number or range between any of these two values, can be sorted per second.

Cell Surface Markers

In some embodiments, the methods utilize the tagging and staining of cell surface markers for cell sorting. In some embodiments, the methods utilize the tagging of cell surface markers for magnetic depletion of cells not of interest, interfering cells, and debris. In some embodiments, the methods include one or more of determining genotype of the patient based on the obtained sequence information; determining phenotype of the patient based on the obtained sequence information; determining one or more genetic mutation of the patient based on the sequence information; and predicting susceptibility of a patient to one or more diseases. At least one of the one or more diseases is cancer or a hereditary disease.

FACS can sort cells based on the tagging and staining of cell surface markers using antibodies that target detectable cell markers. The antibodies include monoclonal and polyclonal antibodies that are coupled to fluorophores. Detectable cell markers include cell surface markers on cells of interest. In some embodiments, magnetic depletion can be based on the tagging of cell surface markers with magnetic beads with antibodies, including monoclonal antibodies and polyclonal antibodies, conjugated to their surfaces targeting cells not of interest, interfering cells, and/or debris. Non-limiting examples of cells surface markers include CD surface markers, growth factor/cytokine, chemokine receptors, nuclear receptors, and other receptors. Examples of cell surface markers include, but are not limited to, ALCAM; CD166; ASGR1; BCAM; BSG; CD147; CD14; CD19; CD2; CD200; CD127 BV421; CD25 BB515; CD161 PE; CD45RA PerCP-Cy™5.5; CD15S AF647; CD4 APC-H; CD4; CD25; CD127; CD45RA; CD15S; CD161; CD3; EpCAM; CD44; and Her2/Neu. Examples of growth factors/cytokines, chemokine receptors include ACVR 1B; ALK4; ACVR2A; ACVR2B; BMPR1A; BMPR2; CSF1R; MCSFR; CSF2RB; EGFR; EPHA2; EPHA4; EPHB2; EPHB4; and ERBB2. Examples of nuclear receptors include androgen receptor; CAR; ER Alpha; ER Beta; ESRRA; ESRRB; ESRRG; FXR; Glucocorticoid Receptor; LXR-a; LXR-b; PPARA; PPARD; PPARG; PXR; SXR; Estrogen Receptor Beta; Progesterone Receptor; RARA; RARB; RARG; RORA; RXRA; RXRB; THRA; THRB; and Vitamin D3 Receptor. Examples of other receptors include AGER; APP; CLEC12A; MICL; CTLA4; FOLR1; FZD1; FRIZZLED-1; KLRB1A; LRPAP1; NCR3; NKP30; OLR1; PROCR; PTPN1; SOX9; SCARB2; TACSTD2; TREM1; TREM2; TREML1; and VDR.

Microtiter Plates

In some embodiments, the methods include enriching a sample comprising a plurality of cells for cells of interest to produce an enriched cell sample, wherein enriching the sample comprises focusing cells of interest in the sample; isolating one or more cells of interest in the enriched cell sample with a flow cytometer and depositing the one or more cells of interest into one or more microtiter plates; and obtaining sequence information of one or more polynucleotides from each of the one or more isolated cells. In some embodiments, a microtiter plate can have at least 96 wells.

In some embodiments, sorting cells of interest (e.g., at 116B of FIG. 1B) from the enriched cell sample includes depositing the one or more cells of interest into one or more microtiter plates. Time course data can be collected while the cells are in the one or more microtiter plates. The size of the microtiter plate can vary. For example, the microtiter plate can have at least 96 cells. In some embodiments, the microtiter plate has, or has about, 96 wells, 384 wells, 1536 wells, 3456 cells, 9600 wells, or a number or a range between any two of these values. In some embodiments, cells of interest can be sorted into 384-well and 1536-well microtiter plates and microtiter plates with higher well density, for example, about 2000, 2500, 3000, 3500, 4000, 5000, 6000, 7500, 10000, 12500, 15000, 20000, 30000, 40000, 50000, 60000, 75000, 100000 wells per microtiter plate.

Cell Lysis

Each well of the one or more microtiter plates can contain a cell lysis buffer, for example a detergent-based single-cell lysis buffer. Each well can also contain RNA stabilizers, molecular labels, cell-specific labels, or a combination thereof. The plates with sorted cells can be kept frozen at −80° C. before further processing.

In certain embodiments, preferential lysis results in lysis of at least 1%, 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 95%, 99%, or a number or range between any two of these values, of cells of interest, e.g., red blood cells or fetal nucleated red blood cells, and lysis of less than 50%, 40%, 30%, 20%, 10%, 5%, 1%, or a number or range between any two of these values, of cells not of interest, e.g. maternal white blood cells or maternal nucleated red blood cells.

Each cell can be lysed, and the mRNA content indexed with a unique sample and molecular index during a reverse transcription step. The identification of cellular composition can utilize 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 2000, 3000, 4000, 5000, 6000, 7500, 10000 or a number or a range between any two of these values, genes. One sequencing library can be generated and run on a sequencing instrument. In some embodiments, prior to generating the sequencing library, products can be pooled from about 2, 3, 4, 5, 6, 7, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 40, 50, 60, 75, 100, 200, 300, 400, 500, 600, 760, 1000, 2000, 3000, 4000, 5000, 6000, 7500, 10000, or a number or a range between any two of these values, microtiter plates, or a number of microtiter plates between any two of these values. More than one sequencing library can be generated. For example, about 2, 3, 4, 5, 6, 7, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 40, 50, 60, 76, 100, 200, 300, 400, 500, 600, 760, 1000, 2000, 3000, 4000, 5000, 6000, 7500, 10000, or a number or a range between any two of these values, sequencing libraries can be generated. Any number of sequencing libraries between two of these values can be generated.

Polynucleotide Targets

In some embodiments, the methods include enriching a sample comprising a plurality of cells for cells of interest to produce an enriched cell sample, wherein enriching the sample comprises focusing cells of interest in the sample; isolating one or more cells of interest in the enriched cell sample; and obtaining sequence information of one or more polynucleotides from each of the one or more isolated cells, wherein the one or more polynucleotides include RNA or mRNA. In some embodiments, the sequence information includes transcript counts of at least 10 genes. The RNA can be obtained from exosome.

Non-limiting examples of polynucleotide targets, for example gene targets and mRNA targets, include CD4, FOX01, CD45RO, MYC, IL1R2, PRF1, GZMK, LGALS1, IL17F, IL23R, LYNX1, PRDM1, SELL, SMAD4, ICOS, IKZF5, RORC, AHRR, CTLA4, ITGB7, ENTPD1, CCR8, TSHR, TGFB2, IL12A, IL7R, HLA-DMA, CCRS, TIAF1, BCL6, BHLHE40, CXCR4, and CD307c. Other non-limiting examples of polynucleotide targets include CD3D, GSTP1, TCF7, CD3E, RNB6, RB1, MYB, CD3G, KRT8, CDH1, ERBB3, ERBB2, TCTN1, ESR1, CDKN1A, and TFF3. Other non-limiting examples of polynucleotide targets include ABCB1, ABCG2, ADAM23, AKT1, APC, AR, ATM, BAD, BCL2, BIRCS, BRCA1, BRCA2, C4A, CA12, CCNA1, CCND1, CCND2, CCNE1, CDH1, CDH13, CDK2, CD326, CDKN1A, CDKN1C, CDKN2A, CSF1, CST6, CTNNB1, CTSD, EGF, EGFR, EMAP-2, ERBB2, ERBB3, ESR1, ESR2, FOXA1, GATA3, GLI1, GPI, GRB7, GSTP1, HIC1, HPRT1, ID1, IGF1, IGF1R, IGFBP3, IL6, JUN, KRT18, KRT19, KRTS, KRT8, LAMP1, MAPK1, MAPK3, MAPK8, MGMT, MKI67, MLH1, MMP2, MMP9, MUC1, MYB, MYC, NME1, NOTCH1, NR3C1, PGR, PLAU, PRDM2, PSMB2, PSMB4, PTEN, PTGS2, PYCARD, RAB7A, RARA, RARB, RASSF1, RB1, REEP5, RNB6, SERPINE1, SFN, SFRP1, SLC39A6, SLIT2, SNAI2, SRC, TBC1D9. TCTN1, TFF3, TGFB1, THBS1, TP73, TWIST1, VEGFA, XBP1, CD3E, CD3G, CD3G, TCF7, ALCAM, CD25, ITGA6, THY1, PROM1, and CXCR4. Other non-limiting examples of polynucleotide targets include BRCA1, BRCA2, TP53, PTEN, MSH2, MLH1, MSH6, PMS2, EPCAM, APC, RB1, MEN1, RET, and VHL.

The polynucleotide targets can be related to blood and lymph diseases; cancers; the digestive system; ear, nose, and throat; diseases of the eye; female-specific diseases; male-specific diseases; glands and hormones; heart and blood vessels; diseases of the immune system; male-specific diseases; muscle and bone; neonatal diseases; the nervous system; nutritional and metabolic diseases; respiratory diseases; and/or skin and connective tis sue.

Sample and Cells of Interest

In some embodiments, the methods include enriching a sample comprising a plurality of cells for cells of interest to produce an enriched cell sample, wherein enriching the sample comprises focusing cells of interest in the sample; isolating one or more cells of interest in the enriched cell sample; and obtaining sequence information of one or more polynucleotides from each of the one or more isolated cells. The type of the sample is not limited. For example the sample can be, or comprise, a clinical sample, a biological sample, an environmental sample, or a combination thereof. For example, the sample can include one or more of a biological fluid, tissue and cell from a patient. In some embodiments, the sample can include blood, urine, cerebrospinal fluid, pleural fluid, amniotic fluid, semen, saliva, bone marrow, a biopsy sample, or a combination thereof. In some embodiments, the cells of interest can include stem cells, cancer cells, blood cells, peripheral blood mononuclear cells, circulating tumor cells (CTCs), breast cancer cells, cells at a cell cycle phase of desire, or a combination thereof. In some embodiments, cells of interest in the sample are, or are about 0.0001%, 0.0005%, 0.001%, 0.005%, 0.01%, 0.1%, 0.5%, 1%, 10%, or a number or a range between any two of these values, of total number of cells in the sample. In some embodiments, fewer than 1000, 500, 250, 100, 50, 25, 10, 5, or a number or a range between any two of these values, cells of interest can be isolated from the enriched cell sample.

As used herein, the term “cells of interest” refer to cells being studied. Because cells of interest can be rare and the concentration of cells of interest in the sample can be low, the enrichment of cells of interest can be advantageous in order to speed up the isolation of cells of interest using, for example, cell sorting. Cells of interest can be enriched using focusing, such as acoustic focusing, and magnetic depletion of cells not of interest and interfering cells and debris. Non-limiting examples of cells of interest in samples include cells expressing a malignant phenotype; tumor cells, such as tumor cells which have been shed from tumor into blood or other bodily fluids or bone marrow; benign tumor cells; cancer cells; cancer cells in peripheral blood; thyroid cancer cells; breast cancer cells; circulating tumor cells (“CTCs”); leukemia cells; cancer stem cells; single cells from different cell cycle phases (G0/G1, S, G2); sperms bearing X and Y chromosomes; stem cells; fetal or adult stem cells; multipotent stem cells; nucleated red blood cells (“NRBC”) in Thalassemia patients; fetal cells, such as fetal cells in maternal peripheral blood; fetal nucleated red blood cells (“FNRBC”) in the maternal circulation; and cells characterized by CD71, CD8, CD34, or CD133. Samples can comprise cells from a mixed cancer cell sample.

Other examples of cells of interest include, but are not limited to, the following cells: circulating endothelial cells; cells infected with a virus, such as cells infected by HIV, cells transfected with a gene of interest; and aberrant subtypes of T-cells or B-cells present in the peripheral blood of subjects afflicted with autoimmune or autoreactive disorders; activated lymphocytes; antigen presenting cells such as monocytes and dendritic cells; pathogenic or parasitic organisms, cells containing intracellular parasites; and cells or microorganisms in dilute fluids like urine.

Non-limiting examples of cell lines include: Jurkat cells, a T-leukemia cell line; SKBR3, an adenocarcinoma derived breast cancer cell line known for overexpression of Her2/neu; T47D, a ductal carcinoma derived breast cancer cell line that demonstrates low to intermediate Her2/neu expression; and HeLa.

The methods disclosed herein can be applicable to samples with low concentration of cells of interest, for example samples with cells of interest. Cells of interest can be rare. In some embodiments, cells of interest are cells that account for less than 0.0001%, 0.0005%, 0.001%, 0.005%, 0.01%, 0.1%, 0.5%, 1%, 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%, 10%, or a number or a range between any two of these values, of the total number of cells in a sample. In some embodiments, the sample is a fluid sample having no more than 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 50, 100, 250, 500, 1000, or a number or a range between any two of these values, cells of interest per milliliter of the sample. The fluid sample can comprise, or be, blood (e.g., whole blood, serum, or plasma), urine, saliva, cerebrospinal fluid, pleural fluid, amniotic fluid, semen, or any combination thereof. In some embodiments, cells of interest are, or are about 1 in 10, 10², 10³, 10⁴, 10⁵, 10⁶, 10⁷, 10⁸, 10⁹, 10¹⁰, 10¹¹, 10¹², 10¹³, 10¹⁵, 10²⁰, or a number or a range between any two of these values, of total cells in the sample or per milliliter of the fluid sample. The size of cells of interest can vary. For example, the diameter of the cells of interest can be about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100 micrometers, or a number or a range between any two of these values.

According to some embodiments, removal of cells not of interest (e.g., at 108B of FIG. 1B) removes at least 1%, 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%, 10%, 15%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 95%, 99%, or a number or a range between any two of these values, of the cells not of interest and/or debris originally present in the sample. In some embodiments, focusing the cells of interest (e.g., acoustic focusing at 112B of FIG. 1B) increases the concentration of cells of interest in the sample by at least 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 10²%, 10³%, 10⁴%, 10⁵%, 10⁶%, 10⁷%, 10⁸%, 10⁹%, 10¹⁰%, 10¹¹%, 10¹²%, 10¹³%, 10¹⁵%, 10²⁰%, or a number or a range between any two of these values. For example, focusing of the cells of interest (e.g., via acoustic focusing at 112B of FIG. 1B) can increases the speed of cell sorting (e.g., at 116B of FIG. 1B) by at least 10-folds, 20-folds, 30-folds, 40-folds, 50-folds, 60-folds, 70-folds, 80-folds, 90-folds, 10²-folds, 10³-folds, 10⁴-folds, 10⁵-folds, 10⁶-folds, 10⁷-folds, 10⁸-folds, 10⁹-folds, 10¹⁰-folds, 10¹¹-folds, 10¹²-folds, 10¹³-folds, 10¹⁵-folds, 10²⁰-folds, or a range or a number between any two of these values.

Sequencer

Many methods, devices and systems are available for sequencing polynucleotides, and can be used for obtaining sequence information of the polynucleotides from the isolated cells in the methods disclosed herein. For example, non-limiting examples of sequencing instruments include GS FLX Titanium , GS Junior, and GS FLX+ systems (454 Life Sciences, Bradford, Conn.), Solexa system, Genome Analyzer IIx, MiSeq, HiSeq, and HiScanSQ (Illumina, Inc., San Diego, Calif.), the SOLiD™ (Sequencing by Oligonucleotide Ligation and Detection) system and Sanger-based DNA sequencers such as the 3500 Genetic Analyzer and Ion Torrent Sequencing systems such as the Personal Genome Machine or the Proton Sequencer (Thermo Fisher Scientific, Waltham, Mass.), and nanopore sequencing systems (Oxford Nanopore Technologies, Oxford, united Kingdom).

Data Analysis

In some embodiments, the methods include enriching a sample comprising a plurality of cells for cells of interest to produce an enriched cell sample, wherein enriching the sample comprises focusing cells of interest in the sample; isolating one or more cells of interest in the enriched cell sample; and obtaining sequence information of one or more polynucleotides from each of the one or more isolated cells by sequencing the molecularly indexed polynucleotide library. Sequencing the molecularly indexed polynucleotide library can, in some embodiments, include deconvoluting the sequencing result from sequencing the library, possibly using a software-as-a-service platform.

FIG. 10 is a flowchart showing non-limiting exemplary steps of data analysis 1000 for use, for example, at 124B of FIG. 1B. Data analysis can be provided in a secure online cloud environment. In some embodiments, data analysis can be performed using a software-as-a-service platform. Non-limiting examples of secure online cloud environments include the Seven Bridges Genomics platform. The Seven Bridges Genomics platform is a non-limiting example of a software-as-a-service platform.

As shown in FIG. 10, data analysis 1000 starts at 1004. At 1008, a sequencing result is received from, for example, 122B of FIG. 1B. Non-limiting examples of the formats of the sequencing result received from 122B of FIG. 1B include EMBL, FASTA, and FASTQ format. The sequencing result can include sequence reads of a molecularly indexed polynucleotide library. The molecularly indexed polynucleotide library can include sequence information of a plurality of single cells. Sequence information of multiple single cells can be deconvoluted by the following steps. At 1012 the sequences of the adaptors used for sequencing at 122B are determined, analyzed, and discarded for subsequent analysis. The one or more adaptors can include the adaptor 234 and 236 in FIG. 2.

At 1016, the sequencing result of a molecularly indexed polynucleotide library is demultiplexed. Demultiplexing can include classifying the sequence reads as belonging to one of a plurality of single cells. Classifying the sequence reads as belonging to one of a plurality of single cells can be based on the label region 214, for example the sample label 220. The sequence reads belonging to one mRNA can be distinguished from those belonging to another mRNA based on the label region 214, for example the molecular label 218. At 1020, sequence reads can be aligned to mRNA sequences using an aligner. Non-limiting examples of the aligner used at 1020 include the Bowtie aligner, ClustalW, BLAST, ExPASy, and T-COFFEE. At 1024, the digital gene expression profile for each cell is reconstructed by counting, for each mRNA sequence, the number of different molecular labels 218 associated with the mRNA sequence for the cell. Consequently, digital counting of mRNAs in a single cell is achieved. The output of data analysis 1000 can include a spreadsheet of read alignment summaries and molecular label counts for each gene in each cell. Data analysis 1000 ends at 1028.

EXAMPLES

Some aspects of the embodiments discussed above are disclosed in further detail in the following examples, which are not in any way intended to limit the scope of the present disclosure.

Example 1 Comprehensive High-Throughput Single-Cell Analysis Combining Proteomic and Genomic Information: Flow Sorting, Cell & mRNA Indexing, and Sequencing Analysis

This example demonstrates comprehensive high-throughput single-cell analysis combining proteomic and genomic information.

Peripheral blood cells were stained with cell surface markers to identify rare regulatory T cell subpopulations including naïve, effector, and non-suppressive regulatory T cells. Cells sampled at various time points were individually sorted into 96-well Precise™ encoding plates using a BD FACSJazz™ cell sorter after sample enrichment with BD Imag™ magnetic separation, thereby increasing sort speed and quality. Each cell was lysed, and the mRNA content was molecularly indexed with a unique sample and molecular index during the reverse transcription step. We interrogated approximately 100 genes that were carefully selected for the identification of cellular composition. After pooling the products from eight 96-well Precise™ encoding plates, three sequencing libraries were generated and run on an Illumina® MiSeq instrument. A streamlined computation pipeline was created, and automated data analysis was performed on the Seven Bridges™ Genomics Platform. These results demonstrate simple and unambiguous de-convolution of cellular subtypes using a high-throughput single-cell genomic sequencing approach combined with the powerful single-cell sorting capabilities of BD FACS analysis Instrumentation.

FIGS. 11A-G are plots showing gating of single cells for next generation sequencing and a legend therefore. Peripheral blood mononuclear cells (“PBMCs”) were collected by CPT tube (Cat. No. 362761). The PBMCs were stained with an antibody cocktail containing white blood cell surface markers: CD127 BV421, CD25 BB515, CD161 PE, CD45RA PerCP-Cy™5.5, CD15S AF647, CD4 APC-H (BD), along with the BD Imag™ CD4 T Lymphocyte Enrichment Kit (Cat. No. 557939). The regulatory T cells were identified by CD4+CD25+CD127− staining. The Treg cell populations were further subdivided by two cell surface markers CD45RA and CD15S into naive Tregs (nTregs CD45RA+CD15S−), effector Tregs (eTregs CD45−CD15S+), and non-suppressive Tregs (CD45RA−CD15S−). The non-suppressive Tregs were further divided into CD161+ and CD161− subpopulations. Non-Tregs cells were also collected into CD45RA+ and CD45RA− subpopulations.

FIGS. 12A-B are bar graphs showing the number of reads and transcripts obtained from single cells. FIG. 12A is a plot showing high sequencing success starting from individual cells sorted into a Precise™ plate. Grey bars indicate possible failures based on significantly lower total read counts. Plate indexing allows multiple plates, up to 4,000 cells, to be pooled into a single MiSeq run if desired. FIG. 12B is a plot showing average transcript count across 96 single cells from the same plate as shown in Panel A after the reads being de-convoluted. Data illustrates absolute mRNA quantitation in single cells across a large measurement range. The success of sorted single-cell sequencing was greater than 95%. Single cells were sorted across a range of time after the samples were stained. 1 hour data point is shown.

FIG. 13 is a plot of counting reads vs. molecules showing that Precise™ assay corrects for PCR bias. The Precise™ assay corrects for PCR amplification bias and allows for absolute counting of transcripts. Counting the number of molecules significantly reduces the noise level associated with counting reads.

FIGS. 14A-B are principal component analysis (“PCA”) plots of Tregs vs. non-Tregs and naïve vs. effector Tregs showing that surface marker phenotypes correlate with molecular profiles. Principal component analysis was used to cluster the individual cells using their molecular counts for expression across the panel of genes. The identity of each cell was recorded by index sorting on a BD FACSJazz, which was represented by each colored point.

FIGS. 15A-C are PCA single-cell plots of CD45RA⁻CD15S⁺, CD161⁻ Non-Suppressive Tregs, and CD45RA⁺CD15S⁻ Effector Tregs across time points.

Utilizing the acoustic focusing device in advance of the flow cytometric cell sorting enables the enrichment of cells of interest (less than 1% of the starting population of cells in the sample). This provides for a reduction of sorting time from hours to minutes. This advantageously provides for populations of live cells that can be analyzed at the same point in their life cycle and for a larger population of viable cells.

The example demonstrates low-cost, high-throughput, single-cell genomic sequencing using the BD FACSJazz cell sorting instrument in conjunction with the Cellular Research Precise™ workflow. The assay has a high success rate—greater than 95% for single-cell sorting and sequencing. It is fast—one 96-well plate of single cells can be sorted within 15 minutes with enrichment, which minimizes the undesired gene expression changes after sample preparation.

Altogether, these data indicate that the combination of flow single-cell sorting with single-cell barcoding provides a powerful tool for linking cell phenotype with molecular transcript profile.

Example 2 The New BD FACSseg™ Cell Sorter for High-Throughput, High-Precision, Quantitative, ingle Cell Gene Expression Profiling

This example demonstrates the use of a cell sorter for high-throughput, high-precision, quantitative single cell gene expression profiling. In particular, this example shows simple and unambiguous deconvolution of cellular types using a high-throughput single cell genomic sequencing approach, for example the Cellular Research Precise™ Assay, combined with a single cell sorter, for example the BD FACSseq™ cell sorter.

In this example, complete workflows for single cell sorting for cell cycle analysis and cell surface marker staining applications are disclosed. In particular, this example demonstrates: 1) elimination of dead cells from collection by propidium iodide (PI) staining; 2) isolation of single cells from different cell cycle phases (G0/G1, S, G2) with Hoechst dye staining; 3) accuracy of sorting cells by depositing different numbers of cells (1 cell, 5 cells, 10 cells, 20 cells, and 50 cells) per well in a 96-well plate; and 4) isolation of single cells from a mixed cancer cell sample based on their specific cell surface marker staining. The gene expression profiles of the sorted cells were analyzed and generated using the Precise™ Assay and next generation sequencing.

FIGS. 16A-C are plots showing cell cycle analysis of single cells cells. Jurkat cells, a T-leukemia cell line, were cultured to log phase and stained with Hoechst 34580 dye (5 mg/mL) for half an hour, and PI (Cat. No. 556463, 5 mg/mL) for 10 minutes at room temperature. The cultured Jurkat cells were analyzed using a BD FACSseq™ sorter. FIG. 16A is a plot of trigger pulse width vs. scatter height showing that single cells were identified based on their composite scatters. Propidium iodide (PI) dye is a membrane permeant DNA binding dye that can be excited by a violet laser. FIG. 16B is a plot of PI median fluorescence intensity (“MFI”) vs. scatter height showing that cells stained positive with PI are dead or dying and can be excluded from collection. The exclusion of dead cells ensures that only viable, healthy representative cells enter the genomic workflow for analysis. FIG. 16C is a histogram plot of count vs. Hoechst 34580 MFI showing the Hoechst staining signal of live cells. FIG. 16C shows the distribution of G0/G1, S, and G2/M cell cycle phases based on their differential Hoechst dye fluorescence staining intensity.

FIGS. 17A-B are plots showing accurate delivery of single cells and small numbers of cells. The BD FACSseq™ sorter can accurately deliver single cells or a small number of cells of interest. And the phenotype information of the sorted cells can be recorded by the index sorting feature of the BD FACSseq™ sorter. Single or small numbers (5, 10, 20, and 50) of fixed Jurkat cells stained with 4′,6-diamidino-2-phenylindole (“DAPI”) were sorted in replicates into 96-well plates. FIG. 17A are images of representative wells, as visualized on a fluorescence microscope, and FIG. 17B are plots of DAPI MFI vs. scatter height showing phenotype information of the sorted cells recorded by index sorting.

FIGS. 18A-B are plots showing single cell isolation with a BD FACSseq™ cell sorter. A cancer cell sample with Jurkat cells, and T47D cells, a breast cancer cell line, mixed at a 80%:20% ratio, was stained with EpCAM BV421 and CD3 BV650 antibodies and run on a BD FACS seq sorter. FIG. 18A is a plot of trigger pulse width vs. scatter height showing that single cells were gated by composite scatter. FIG. 18B is a plot of CD3 BV650 vs. EpCAM BV421 showing the separation of the two types of cells by their specific surface marker staining. Jurkat cells were only stained positive with CD3, while T47D were only stained with EpCAM. The mixed cells were randomly sorted into each well of a 96-well Cellular Research Precise™ encoded plate, with one single cell in each well.

FIGS. 19A-C show PCA clustering and gene expression profiles of Jurkat cells and T47D cells separated by with a BD FACSseq™ cell sorter shown in FIGS. 18A-B. FIG. 19A is a PCA plot showing clustering of Jurkat cells and T47D cells into two clusters. FIGS. 19B-C are bar graphs showing gene expression profiles in cluster 1 of Jurkat cells and cluster 2 of T47D cells. FIGS. 19B-C show that the genes highly expressed in two clusters corresponded well with Jurkat cells (cluster 1) and T47D cells (cluster 2).

Altogether, these data demonstrate a simple, fast method using a new, easy-to-use sorter to select and isolate single cells for high-resolution genomic studies, including next generation sequencing, qPCR, and microarray analysis. The sorter provides a powerful tool for studying complex samples at the individual cell level.

Example 3 Single Cell Gene Expression Profiles of Her2/neu-Expressing Cancer Cells Using Molecular Indexing Technology

This example demonstrates the determination of single cell gene expression profiles of Her2/neu-expressing cancer cells using molecular indexing technology.

The expression level of Her2/neu (Cerb-B2) in breast cancer is a fundamental classification in the diagnostic process. The overexpression of this oncogene correlates with reduced survival and can be a driving determinant of treatment. However, the mechanisms at work in the aggressive breast cancers associated with Her2/neu overexpression are poorly understood. Conventional gene profile studies utilizing bulk tissue can be hampered by poor signal-to-noise ratios, making it difficult to appreciate changes in gene expression. A single-cell analysis approach can overcome these problems and reveal a more comprehensive data set. The aim of this example was to identify genes that are up-regulated or down-regulated in individual cells that overexpress Her2/neu.

Single cells from a variety of cell lines known to express different levels of Her2/neu were sorted into 96-well plates using the BD FACSseg™ cell sorter. The cell lines used included SKBR3, an adenocarcinoma derived breast cancer cell line known for overexpression of Her2/neu, and T47D, a ductal carcinoma derived breast cancer cell line that demonstrates low to intermediate Her2/neu expression. Negative control cell lines (HeLa and Jurkat) were also included in the screening. These sorted single cells were processed using a molecular indexing technology for breast cancer specific genes. This gene expression panel includes 100 genes associated with breast cancer. The resulting gene profiles were then correlated with Her2/neu expression levels on individual cells.

Cell Culture. HeLa cells, a cervical epithelial adenocarcinoma cell line, were cultured with minimum essential medium (“MEM”). Jurkat cells, a T-leukemia cell line, were cultured with Roswell Park Memorial Institute medium (“RPMI”) 1640 media. SKBR3 cells, a metastatic breast adenocarcinoma cell line, and T47D cells, a ductal breast carcinoma cell line, were both cultured with Dulbecco's minimum essential medium (“DMEM”). All media were supplemented with 10% heat-inactivated fetal bovine serum (“FBS”) and 1% Penicillin/Streptomycin solution. In addition, all cell lines were harvested using Gibco StemPro® Accutase® prior to staining.

Flow Cytometry. HeLa, Jurkat, SKBR3, and T47D human cancer cell lines were cultured to log phase, harvested, and counted. 1×106 cells for each cell line were stained with BD HORIZON™ BV421 Her-2/Neu (BD custom antibody) and BD HORIZON™ BV786 CD44 (BD Biosciences, Cat. No. 564942) for 30 minutes, washed, followed by staining with the cell viability dye Propidium Iodide (BD Biosciences, Cat. No. 51-66211E) for 10 minutes at room temperature. Samples were then analyzed and sorted using a BD FACSseq cell sorter. Based on Her2/Neu protein expression, live single cells were sorted directly into pre-filled Cellular Research Precise™ breast cancer 96-well assay plates.

Sequencing Library Generation and Data Analysis. Cellular Research Precise™ plates are preloaded with a detergent-based cell lysis buffer along with RNA stabilizers, molecular indices, and sample-specific barcodes. After sorting cells into plates, lysed cells were kept frozen at −80° C. before further processing. These samples were then processed according to the Cellular Research Precise™ assay protocol to pool samples, amplify target genes and generate a sequencing library. The library was then sequenced on an Illumina® MiSeq instrument. A streamlined computation pipeline was created, and automated data analysis was performed on the Seven Bridges Genomics Platform. Data output from Seven Bridges was then analyzed using the Qlucore software package.

FIGS. 20A-B and 21A-D show relative expressions of CD44 and Her2/Neu. FIGS. 20A-B are plots of CD44 BV786 vs. Her2/Neu BV421 and Counts vs. Her2/Neu BV421 showing that each cell type, Jurkat, T47D, HeLa, and SKBR3, was clearly defined by relative expression of CD44 and Her2/Neu.

FIGS. 21A-D are plots showing that Jurkat cells in FIG. 21A, HeLa cells in FIG. 21B, T47D cells in FIG. 21C, and SKBR3 in FIG. 21D were sorted based on Her2/Neu expression. Three populations within each cell type were collected: Her2/Neu low, Her2/Neu intermediate, and Her2/Neu high. Single cells were sorted directly into each well of a BD Precise™ breast cancer 96-well assay plate. Each quadrant shows the gating strategy used to ensure that sorted cells were live single cells with the desired Her2/Neu expression profile.

FIG. 22 is a table showing the breast cancer genes analyzed with the Cellular Research Precise™ assay. Using the Cellular Research Precise™ gene library (99 genes) as a base plus 11 additional genes, this example interrogated 110 target genes. The genes that are shaded in light grey designate targets that were included in the final data analysis. The genes that are not shaded were not detected above threshold and thus excluded from the final data analysis. The products from four 96-well BD Precise assay plates (4×96 samples) were pooled, and one sequencing library was generated.

FIGS. 23, 24A-B, and 25A-G show automated data analysis was performed using the Seven Bridges Genomics Platform. Of 110 target genes, the expression levels of 57 were detectable. FIG. 23 is a heat map showing the analysis of the expressed target genes demonstrated a clear distinction between the four different cell types. Within each cell type, the subpopulations defined by Her2/Neu protein expression levels, did not exhibit significant differences in gene expression patterns.

FIGS. 24A-B are PCA plots showing clustering by cell type with random Her2/Neu expressions supporting the heat map results. FIG. 24A is a PCA plot shows clustering analysis by cell type. The clustering analysis shows that the four cell types clearly segregated into four distinct clusters. FIG. 24B is a PCA plot showing that Her2/Neu expression had no discernable pattern within each cell type cluster.

FIGS. 25A-G are expression plots showing that step-wise increases in ERBB2, BRCA1, CDK2, MUC1, CDH1, CTSD, and CTSD. FIG. 25A shows that ERBB2, the mRNA precursor to Her2/Neu, had extreme over-expression in SKBR3, relatively low expression in T47D, and little to no expression in HeLa and Jurkat cells. Closer examination of ERBB2 expression in T47D, shows a step-wise increase that correlates nicely to Her2/Neu protein expression by flow cytometry. Similar step-wise increases in other gene products that seemed to correlate with Her2/Neu expression were detected in BRCA1, a tumor suppressor, shown in FIG. 25B, CDK2, a kinase, shown in FIG. 25C, MUC1, a mucin, shown in FIG. 25D, CDH1, a cadherin, shown in FIG. 25E, CTSD, a protease shown in FIG. 25F, and XBP1, a transcription factor, shown in FIG. 25G.

These data show that differential expression of Her2/Neu among different cancer cell lines can be detected at both the protein level and the mRNA level. T47D cells exhibited an increase in mRNA levels of ERBB2 that demonstrated a modest correlation with the concurrent increase in protein levels of Her2/Neu between sorted populations of Her2/Neu low, Her2/Neu intermediate and Her2/Neu high expressing cells. Similar concurrent increases in mRNA levels of BRCA1, CDK2, MUC1, CDH1, CTSD, and XBP1 also showed a modest correlation to Her2/Neu protein expression levels. The BD FACSseq™ cell sorter coupled with the Cellular Research Precise™ Assay provides a robust and cost-effective workflow from single cell selection and isolation to downstream quantitative mRNA analysis. This workflow is a powerful tool that combines phenotypic and gene expression profiles for studying complex biological samples at the single cell level.

The identification of genes associated with Her2/neu overexpression on a single cell level shed light on the mechanisms at play in these aggressive cancers that are known to comprise up to 30% of all breast cancers. Altogether, these data, generated by combining single-cell sorting and molecular sorting, shed light on the mechanisms at play in these aggressive cancers that are known to comprise up to 30% of all breast cancers.

In at least some of the previously described embodiments, one or more elements used in an embodiment can interchangeably be used in another embodiment unless such a replacement is not technically feasible. It will be appreciated by those skilled in the art that various other omissions, additions and modifications may be made to the methods and structures described above without departing from the scope of the claimed subject matter. All such modifications and changes are intended to fall within the scope of the subject matter, as defined by the appended claims.

With respect to the use of substantially any plural and/or singular terms herein, those having skill in the art can translate from the plural to the singular and/or from the singular to the plural as is appropriate to the context and/or application. The various singular/plural permutations may be expressly set forth herein for sake of clarity.

It will be understood by those within the art that, in general, terms used herein, and especially in the appended claims (e.g., bodies of the appended claims) are generally intended as “open” terms (e.g., the term “including” should be interpreted as “including but not limited to,” the term “having” should be interpreted as “having at least,” the term “includes” should be interpreted as “includes but is not limited to,” etc.). It will be further understood by those within the art that if a specific number of an introduced claim recitation is intended, such an intent will be explicitly recited in the claim, and in the absence of such recitation no such intent is present. For example, as an aid to understanding, the following appended claims may contain usage of the introductory phrases “at least one” and “one or more” to introduce claim recitations. However, the use of such phrases should not be construed to imply that the introduction of a claim recitation by the indefinite articles “a” or “an” limits any particular claim containing such introduced claim recitation to embodiments containing only one such recitation, even when the same claim includes the introductory phrases “one or more” or “at least one” and indefinite articles such as “a” or “an” (e.g., “a” and/or “an” should be interpreted to mean “at least one” or “one or more”); the same holds true for the use of definite articles used to introduce claim recitations. In addition, even if a specific number of an introduced claim recitation is explicitly recited, those skilled in the art will recognize that such recitation should be interpreted to mean at least the recited number (e.g., the bare recitation of “two recitations,” without other modifiers, means at least two recitations, or two or more recitations). Furthermore, in those instances where a convention analogous to “at least one of A, B, and C, etc.” is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (e.g., “a system having at least one of A, B, and C” would include but not be limited to systems that have A alone, B alone, C alone, A and B together, A and C together, B and C together, and/or A, B, and C together, etc.). In those instances where a convention analogous to “at least one of A, B, or C, etc.” is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (e.g., “ a system having at least one of A, B, or C” would include but not be limited to systems that have A alone, B alone, C alone, A and B together, A and C together, B and C together, and/or A, B, and C together, etc.). It will be further understood by those within the art that virtually any disjunctive word and/or phrase presenting two or more alternative terms, whether in the description, claims, or drawings, should be understood to contemplate the possibilities of including one of the terms, either of the terms, or both terms. For example, the phrase “A or B” will be understood to include the possibilities of “A” or “B” or “A and B.”

In addition, where features or aspects of the disclosure are described in terms of Markush groups, those skilled in the art will recognize that the disclosure is also thereby described in terms of any individual member or subgroup of members of the Markush group.

As will be understood by one skilled in the art, for any and all purposes, such as in terms of providing a written description, all ranges disclosed herein also encompass any and all possible sub-ranges and combinations of sub-ranges thereof. Any listed range can be easily recognized as sufficiently describing and enabling the same range being broken down into at least equal halves, thirds, quarters, fifths, tenths, etc. As a non-limiting example, each range discussed herein can be readily broken down into a lower third, middle third and upper third, etc. As will also be understood by one skilled in the art all language such as “up to,” “at least,” “greater than,” “less than,” and the like include the number recited and refer to ranges which can be subsequently broken down into sub-ranges as discussed above. Finally, as will be understood by one skilled in the art, a range includes each individual member. Thus, for example, a group having 1-3 articles refers to groups having 1, 2, or 3 articles. Similarly, a group having 1-5 articles refers to groups having 1, 2, 3, 4, or 5 articles, and so forth.

While various aspects and embodiments have been disclosed herein, other aspects and embodiments will be apparent to those skilled in the art. The various aspects and embodiments disclosed herein are for purposes of illustration and are not intended to be limiting, with the true scope and spirit being indicated by the following claims. 

1-20. (canceled)
 21. A system for single-cell polynucleotide sequencing, the system comprising: a sample enricher configured to produce an enriched cell sample from a sample comprising a plurality of cells for cells of interest; a cell isolator configured to isolate one or more cells of interest from the enriched cell sample; and a sequencer configured to obtain sequence information of one or more polynucleotides from each of the one or more isolated cells, wherein the sequencer is configured to obtain sequence information by generating a molecularly indexed polynucleotide library from the one or more isolated cells.
 22. The system of claim 21, wherein the sample enricher is configured to focus the cells of interest in the sample.
 23. The system of claim 22, wherein the sample enricher is configured to focus the cells of interest in the sample into a core stream of cells.
 24. The system of claim 22, wherein the sample enricher is configured to focus the cells of interest in the sample is based on cell size.
 25. The system of claim 22, wherein sample enricher is configured to focus the cells of interest in the sample by acoustic focusing.
 26. The system of claim 25, wherein acoustic focusing comprises: suspending the plurality of cells in the sample in an elongated fluid-filled channel; and exposing the channel to an axial acoustic standing wave field parallel to the direction of flow, wherein the axial acoustic standing wave field drives the plurality of cells to positions of potential force minimum along the center axis of the channel to result in uniformly spaced cells.
 27. The system of claim 21, wherein cell isolator comprises a sorting flow cytometer.
 28. The system of claim 27, wherein sorting flow cytometer is configured to deposit one or more cells of interest in the enriched cell sample into one or more microtiter plates.
 29. The system of claim 21, wherein the one or more polynucleotides comprise ribonucleic acids (RNAs).
 30. The system of claim 29, wherein the RNAs are messenger RNAs (mRNAs).
 31. The system of claim 30, wherein the sequence information comprises transcript counts of at least 10 genes.
 32. The system of claim 21, wherein no more than 500 cells are isolated from the enriched cell sample.
 33. The system of claim 21, wherein obtaining sequence information further comprises sequencing the molecularly indexed polynucleotide library.
 34. The system of claim 33, wherein sequencing the molecularly indexed polynucleotide library comprises deconvoluting the sequencing result from sequencing the library.
 35. The system of claim 21, wherein the system further comprises a sample that comprises a biological sample, a clinical sample, an environmental sample, or a biological fluid, tissue or cell from a patient.
 36. The system of claim 21, wherein the cells of interest in the sample comprise stem cells, cancer cells, blood cells, peripheral blood mononuclear cells, circulating tumor cells, breast cancer cells, cells at a cell cycle phase of desire, or a combination thereof.
 37. The system of claim 35, wherein the system is configured to further determine genotype, phenotype, or one or more genetic mutation of the patient based on the obtained sequence information.
 38. The system of claim 35, wherein the system is configured to further predict susceptibility of the patient to one or more diseases.
 39. The system of claim 38, wherein at least one of the one or more diseases is cancer or a hereditary disease.
 40. The system of claim 21, wherein less than 10% of total number of cells in the sample are the cells of interest in the sample. 